Long-term prognosis and its associated predictive factors in patients with eosinophilic gastroenteritis

BACKGROUND Eosinophilic gastroenteritis(EGE)is a chronic recurrent disease with abnormal eosinophilic infiltration in the gastrointestinal tract.Glucocorticoids remain the most common treatment method.However,disease relapse and glucocorticoid dependence remain notable problems.To date,few studies have illuminated the prognosis of EGE and risk factors for disease relapse.AIM To describe the clinical characteristics of EGE and possible predictive factors for disease relapse based on long-term follow-up.METHODS This was a retrospective cohort study of 55 patients diagnosed with EGE admitted to one medical center between 2013 and 2022.Clinical records were collected and analyzed.Kaplan-Meier curves and log-rank tests were conducted to reveal the risk factors for long-term relapse-free survival(RFS).RESULTS EGE showed a median onset age of 38 years and a slight female predominance(56.4%).The main clinical symptoms were abdominal pain(89.1%),diarrhea(61.8%),nausea(52.7%),distension(49.1%)and vomiting(47.3%).Forty-three(78.2%)patients received glucocorticoid treatment,and compared with patients without glucocorticoid treatments,they were more likely to have elevated serum immunoglobin E(IgE)(86.8%vs 50.0%,P=0.022)and descending duodenal involvement(62.8%vs 27.3%,P=0.046)at diagnosis.With a median follow-up of 67 mo,all patients survived,and 56.4%had at least one relapse.Six variables at baseline might have been associated with the overall RFS rate,including age at diagnosis<40 years[hazard ratio(HR)2.0408,95%confidence interval(CI):1.0082–4.1312,P=0.044],body mass index(BMI)>24 kg/m^(2)(HR 0.3922,95%CI:0.1916-0.8027,P=0.014),disease duration from symptom onset to diagnosis>3.5 mo(HR 2.4725,95%CI:1.220-5.0110,P=0.011),vomiting(HR 3.1259,95%CI:1.5246-6.4093,P=0.001),total serum IgE>300 KU/L at diagnosis(HR 0.2773,95%CI:0.1204-0.6384,P=0.022)and glucocorticoid treatment(HR 6.1434,95%CI:2.8446-13.2676,P=0.003).CONCLUSION In patients with EGE,younger onset age,longer disease course,vomiting and glucocorticoid treatment were risk factors for disease relapse,whereas higher BMI and total IgE level at baseline were protective.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Review of Sc microalloying effects in Al-Cu alloys

Artificially controlling the solid-state precipitation in aluminum (Al) alloys is an efficient way to achieve well-performed properties,and the microalloying strategy is the most frequently adopted method for such a purpose.In this paper,recent advances in lengthscale-dependent scandium (Sc) microalloying effects in Al-Cu model alloys are reviewed.In coarse-grained Al-Cu alloys,the Sc-aided Cu/Sc/vacancies complexes that act as heterogeneous nuclei and Sc segregation at the θ′-Al_(2)Cu/matrix interface that reduces interfacial energy contribute significantly to θ′precipitation.By grain size refinement to the fine/ultrafine-grained scale,the strongly bonded Cu/Sc/vacancies complexes inhibit Cu and vacancy diffusing toward grain boundaries,promoting the desired intragranular θ′precipitation.At nanocrystalline scale,the applied high strain producing high-density vacancies results in the formation of a large quantity of (Cu Sc,vacancy)-rich atomic complexes with high thermal stability,outstandingly improving the strength/ductility synergy and preventing the intractable low-temperature precipitation.This review recommends the use of microalloying technology to modify the precipitation behaviors toward better combined mechanical properties and thermal stability in Al alloys.

Theoretical characterization of the temperature-dependent saturation magnetization of magnetic metallic materials

Based on the force-heat equivalence energy density principle,a theoretical model for magnetic metallic materials is developed,which characterizes the temperature-dependent magnetic anisotropy energy by considering the equivalent relationship between magnetic anisotropy energy and heat energy;then the relationship between the magnetic anisotropy constant and saturation magnetization is considered.Finally,we formulate a temperature-dependent model for saturation magnetization,revealing the inherent relationship between temperature and saturation magnetization.Our model predicts the saturation magnetization for nine different magnetic metallic materials at different temperatures,exhibiting satisfactory agreement with experimental data.Additionally,the experimental data used as reference points are at or near room temperature.Compared to other phenomenological theoretical models,this model is considerably more accessible than the data required at 0 K.The index included in our model is set to a constant value,which is equal to 10/3 for materials other than Fe,Co,and Ni.For transition metals(Fe,Co,and Ni in this paper),the index is 6 in the range of 0 K to 0.65T_(cr)(T_(cr) is the critical temperature),and 3 in the range of 0.65T_(cr) to T_(cr),unlike other models where the adjustable parameters vary according to each material.In addition,our model provides a new way to design and evaluate magnetic metallic materials with superior magnetic properties over a wide range of temperatures.

An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

Temperature inversion enables superior stability for low-temperature Zn-ion batteries

It is challenging for aqueous Zn-ion batteries(ZIBs)to achieve comparable low-temperature(low-T)performance due to the easy-frozen electrolyte and severe Zn dendrites.Herein,an aqueous electrolyte with a low freezing point and high ionic conductivity is proposed.Combined with molecular dynamics simulation and multi-scale interface analysis(time of flight secondary ion mass spectrometry threedimensional mapping and in-situ electrochemical impedance spectroscopy method),the temperature independence of the V_(2)O_(5)cathode and Zn anode is observed to be opposite.Surprisingly,dominated by the solvent structure of the designed electrolyte at low temperatures,vanadium dissolution/shuttle is significantly inhibited,and the zinc dendrites caused by this electrochemical crosstalk are greatly relieved,thus showing an abnormal temperature inversion effect.Through the disclosure and improvement of the above phenomena,the designed Zn||V_(2)O_(5)full cell delivers superior low-T performance,maintaining almost 99%capacity retention after 9500 cycles(working more than 2500 h)at-20°C.This work proposes a kind of electrolyte suitable for low-T ZIBs and reveals the inverse temperature dependence of the Zn anode,which might offer a novel perspective for the investigation of low-T aqueous battery systems.

Extension of Flow Behaviour and Damage Models for Cast Iron Alloys with Strain Rate Effect

Cast iron alloys with low production cost and quite good mechanical properties are widely used in the automotive industry.To study the mechanical behavior of a typical ductile cast iron(GJS-450)with nodular graphite,uni-axial quasi-static and dynamic tensile tests at strain rates of 10^(-4),1,10,100,and 250 s^(-1)were carried out.In order to investigate the influence of stress state on the deformation and fracture parameters,specimens with various geometries were used in the experiments.Stress strain curves and fracture strains of the GJS-450 alloy in the strain rate range of 10^(-4)to 250 s^(-1)were obtained.A strain rate-dependent plastic flow model was proposed to describe the mechanical behavior in the corresponding strain-rate range.The available damage model was extended to take the strain rate into account and calibrated based on the analysis of local fracture strains.Simulations with the proposed plastic flow model and the damage model were conducted to observe the deformation and fracture process.The results show that the strain rate has obviously nonlinear effects on the yield stress and fracture strain of GJS-450 alloys.The predictions with the proposed plastic flow and damage models at various strain rates agree well with the experimental results,which illustrates that the rate-dependent plastic flow and damage models can be used to describe the mechanical behavior of cast iron alloys at elevated strain rates.The proposed plastic flow and damage models can be used to describe the deformation and fracture analysis of materials with similar properties.

Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.

Deep-subwavelength single grooves prepared by femtosecond laser direct writing on Si

It is well known that femtosecond laser pulses can easily spontaneously induce deep-subwavelength periodic surface structures on transparent dielectrics but not on non-transparent semiconductors.Nevertheless,in this study,we demonstrate that using high-numerical-aperture 800 nm femtosecond laser direct writing with controlled pulse energy and scanning speed in the near-damage-threshold regime,polarization-dependent deep-subwavelength single grooves with linewidths of~180 nm can be controllably prepared on Si.Generally,the single-groove linewidth increases slightly with increase in the pulse energy and decrease in the scanning speed,whereas the single-groove depth significantly increases from~300 nm to~600 nm with decrease in the scanning speed,or even to over 1μm with multi-processing,indicating the characteristics of transverse clamping and longitudinal growth of such deep-subwavelength single grooves.Energy dispersive spectroscopy composition analysis of the near-groove region confirms that single-groove formation tends to be an ultrafast,non-thermal ablation process,and the oxidized deposits near the grooves are easy to clean up.Furthermore,the results,showing both the strong dependence of groove orientation on laser polarization and the occurrence of double-groove structures due to the interference of pre-formed orthogonal grooves,indicate that the extraordinary field enhancement of strong polarization sensitivity in the deep-subwavelength groove plays an important role in single-groove growth with high stability and collimation.

Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

Nonuniform Dependence on the Initial Data for Solutions of Conservation Laws

In this paper,we study systems of conservation laws in one space dimension.We prove that for classical solutions in Sobolev spaces H^(s),with s>3/2,the data-to-solution map is not uniformly continuous.Our results apply to all nonlinear scalar conservation laws and to nonlinear hyperbolic systems of two equations.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Assessing the role of forest resources in improving rural livelihoods in West Bengal of India

Forest resources play a vital role in supporting the livelihoods of rural communities residing in forest-rich areas.In India,a forest-rich country,a significant proportion of non-timber forest products(NTFPs)is consumed locally,supporting numerous rural communities relying on forests for essential resources,such as firewood,timber,and NTFPs.This study focuses on two forest-dominant districts in West Bengal of India,namely,Jhargram District and Paschim Medinipur District.Furthermore,this study aims to enhance the understanding of forest-dependent communities by comparing the standard of living among different village classes.Thus,we categorized villages into three classes based on the distance from home to forests,including inner villages,fringe villages,and outer villages.Through focus group discussions and household surveys,we explored the sources of local economy,income sources of household,and reasons for economic diversification in different village classes.The study findings confirm that substantial variations existed in the income sources and the standard of living in these villages.Forest income varied dramatically among the three village classes,with inner villages having greater forest income than fringe villages and outer villages.Meanwhile,households in outer villages depended on forests and engaged in diverse economic activities for their livelihoods.Compared with inner and fringe villages,households in outer villages derived a significant portion of their income from livestock.This discrepancy can be attributed to challenges,such as inadequate transportation,communication,and underdeveloped market chains in inner villages.Moreover,these findings emphasize the need to develop sustainable forest management practices,create alternative income-generation opportunities,and improve infrastructure and market access in inner villages,as well as promote economic diversification in outer villages.Through targeted policy measures,these forest-rich regions can achieve improved livelihoods,enhanced standard of living,and increased resilience for their communities.

基于半参数Copula学习的确定性独立筛选研究

This paper is concerned with ultrahigh dimensional data analysis,which has become increasingly important in diverse scientific fields.We develop a sure independence screening procedure via the measure of conditional mean dependence based on Copula(CC-SIS,for short).The CC-SIS can be implemented as easily as the sure independence screening procedures which respectively based on the Pearson correlation,conditional mean and distance correlation(SIS,SIRS and DC-SIS,for short)and can significantly improve the performance of feature screening.We establish the sure screening property for the CC-SIS,and conduct simulations to examine its finite sample performance.Numerical comparison indicates that the CC-SIS performs better than the other two methods in various models.At last,we also illustrate the CC-SIS through a real data example.