Component Maps Research Articles

A novel parent selection strategy for the development of drought-tolerant cotton cultivars

AbstractDrought is a devastating factor for crop production worldwide. Therefore, an experiment was conducted to study genetics for some agro-physiological traits in cotton under drought stress. The 13 parental cotton genotypes along with their 30 F1 hybrids were planted under normal and drought conditions. The mean performance of the genotypes was assessed through principal component and heat map analyses. The principal component analyses revealed 53.99 and 53.15% in the first two principal components of variability for normal and drought conditions, respectively. Heat map analysis revealed that three cotton genotypes i.e. FH-207 × NS-131, FH-207 × KZ-191 and S-15 × AA-703 attained higher values for all the traits except for canopy temperature under drought conditions. These crosses may proliferate to further filial generations to identify transgressive segregates for drought tolerance. The heritable differences of F̅1 and mid-parent showed dominance and non-additive gene action under drought conditions. Heritable differences between F̅1 and P̅1 showed over dominance and partial dominance under drought conditions. Heritable differences between F̅1 and P̅2 indicated negative over dominance and partial dominance for all traits under drought conditions. Proline contents and the bolls per plant showed high heritability and genetic advance through additive gene action. Therefore, these two traits can be used as a means of selection in future breeding programmes of drought tolerance.

A Two-Stage Underwater Enhancement Network Based on Structure Decomposition and Characteristics of Underwater Imaging

Due to the scattering and attenuation of light into the water, the underwater image usually appears with color distortion, blurred details, and low contrast. To address these problems, a novel two-stage underwater image convolutional neural network (CNN) based on structure decomposition (UWCNN-SD) for underwater image enhancement is proposed by considering the characteristics of underwater imaging. Specifically, the raw underwater image is decomposed into high-frequency and low-frequency based on theoretical analysis of the underwater imaging. Then, a two-stage underwater enhancement network including a preliminary enhancement network and a refinement network is proposed. In the first stage, the preliminary enhancement network, which contains the high-frequency and the low-frequency enhancement networks, is proposed. The high-frequency part is enhanced directly by a deep learning network, and the low-frequency enhancement network is based on the underwater imaging, which is integrated transmission map and background light into joint component map. In the second stage, the refinement network is designed to further optimize the color of the underwater image by considering complexity of underwater imaging. The experimental results of synthetic and real-world underwater images/videos demonstrate that the proposed UWCNN-SD method can perform color correction and enhancement on different types of underwater images. The ablation study verifies the effectiveness of each component, and application tests further illustrate that the proposed UWCNN-SD method can obtain underwater images with higher visual quality. The trained model is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wushengcong/UWCNN-SD</uri> .

The technology of augmented reality based on 3D modeling to improve special skills for vocational students in the era of industrial revolution 4.0

Currently, the development of virtual learning technology continues to increase. Virtual technology such as augmented reality is becoming a sophisticated learning innovation. On the other hand, the special abilities of vocational education graduates have decreased relatively. This study aims to: 1) map the components of the need to improve special skills in vocational education graduate students; 2) develop 3D-modeling based augmented reality learning technology; and 3) testing the attractiveness of the 3D-modeling based augmented reality learning technology. The method used is the research and development (R&D) method. Requirement component map data was taken using qualitative methods through questionnaires and observations. Informants at this stage were students in the field of vocational education at State Universities in East Java. Furthermore, expert tests were conducted by material experts in the field of vocational education and online learning media experts. The results of this study include 1) map of the components of the need to improve special skills in vocational education student graduates consisting of idea development (78%), utilization of infrastructure (82%), deepening knowledge (90%), professional organization (88%), and mental stimulation (90 %); 2) the developed 3D-modeling based augmented reality learning technology has an excellent level of attractiveness and feasibility based on online learning media experts and material experts; and 3) learning technology products based on 3D-modeling augmented reality which is developed can increase the special skills of graduate vocational education students.

Blueschist mylonitic zones accommodating syn-subduction exhumation of deeply buried continental crust: the example of the Rocca Canavese Thrust Sheets Unit (Sesia\u2013Lanzo Zone, Italian Western Alps)

The Rocca Canavese Thrust Sheets Unit (RCTU) is a subduction-related mélange that represents the eastern-most complex of the Sesia–Lanzo Zone (SLZ), bounded by the Periadriatic (Canavese) Lineament that separates the Alpine subduction complex from the Southalpine domain. The RCTU is limited to the south by the Lanzo Massif (LM) and to the east by the Eclogitic Micaschists Complex (EMC). Particularly the tectonic contact area of the RCTU, adjacent to the neighbouring SLZ and the LM is characterised by a 100–200-m-thick mylonitic to ultra-mylonitic zone (MZ) that was active under blueschist-to greenschist-facies conditions. Despite the dominant mylonitic structure, some rocks (garnet-bearing gneiss, garnet-free gneiss and orthogneiss) still preserve pre-mylonitic parageneses in meter-sized domains. The scarcity of superposed structures and the small size of relicts impose a detailed microstructural analysis supported by chemical investigation to reconstruct the tectono-metamorphic history of the MZ. Therefore, we integrated the classical meso- and microstructural analysis approach with a novel quantitative technique based on the Quantitative X-Ray Map Analyzer (Q-XRMA), used to classify rock-forming minerals starting from an array of X-ray elemental maps, both at whole thin section and micro-domain scale, as well as to calibrate the maps for pixel-based chemical analysis and end-member component maps, relevant for a more robust conventional geothermobarometer application as well for calculating reliable PT pseudosections. Pre-Alpine relicts are garnet and white mica porphyroclasts in the garnet-bearing gneiss and biotite and K-feldspar porphyroclasts in garnet-free gneiss and orthogneiss, respectively, providing no PT constraints. The Alpine evolution of the MZ rocks, has been subdivided in three deformation and metamorphic stages. The first Alpine structural and metamorphic equilibration stage (D1 event) occurred at a pressure of ca. 1.25–1.4 GPa and at a temperature of ca. 420–510 °C, i.e. under blueschist-facies conditions. The D2 event, characterised by a mylonitic foliation that is pervasive in the MZ, occurred at ca. 0.95–1.1 GPa and ca. 380–500 °C, i.e. under epidote-blueschist-facies conditions. The D2 PT conditions in the MZ rocks are similar to those predicted for the blocks that constitute the RCTU mélange, and they overlap with the exhumation paths of the EMC and LM units. Therefore, the RCTU, EMC and LM rocks became coupled together during the D2 event. This coupling occurred during the exhumation of the different tectono-metamorphic units belonging to both continental and oceanic lithosphere and under a relatively cold thermal regime, typical for an active oceanic subduction zone, pre-dating Alpine continental collision.

Multitemporal Hyperspectral Images Change Detection Based on Joint Unmixing and Information Coguidance Strategy

The richness of spectral information in multitemporal hyperspectral images (MHSIs) offers the possibility to effectively detect subtle changes and properties of grounds. However, severe spectral variabilities and inadequate spatial co-exploitation capabilities limit the performance of existing methods due to differences in acquisition times and conditions. Therefore, this article proposes a strategy of joint unmixing and multitemporal spatial information coguidance (JUC) to fully exploit the spatio-temporal-spectral features. First, a multitemporal joint unmixing method is used to achieve endmembers’ extraction and abundance estimation. Wherein the method adds spectral perturbed regularization when compared to the traditional unmixing strategy, making it robust to spectral variability. Second, we propose a multitemporal coguidance method that highlights the contrast between changed and unchanged regions and removes the noise by transferring the common structure information between the multitemporal first principal component map and the abundance difference maps. It will obtain an enhanced abundance difference maps and achieve effective combination of multitemporal spatial information. The final change result can be obtained by combining and thresholding these enhanced abundance difference maps. Experiments on some data sets demonstrate that the proposed algorithm is sufficiently valid and robust for multitemporal images, especially for data containing spectral variabilities and obvious structures.

Greedy weights for matroids

We introduce greedy weights of matroids, inspired by those for linear codes. We show that a Wei duality holds for two of these types of greedy weights for matroids. Moreover we show that in the cases where the matroids involved are associated to linear codes, our definitions coincide with those for codes. Thus our Wei duality is a generalization of that for linear codes given by Schaathun. In the last part of the paper we show how some important chains of cycles of the matroids appearing, correspond to chains of component maps of minimal resolutions of the independence complex of the corresponding matroids. We also relate properties of these resolutions to chainedness and greedy weights of the matroids, and in many cases codes, that appear.

Modeling and analysis of windmilling operation during mode transition of a turbine-based-combined cycle engine

Mode transition control is a critical issue of Turbine-Based-Combined Cycle (TBCC) engines when the primary thrust providers change from gas turbine engines to ramjets/scramjets. During mode transition, windmilling operation is inevitable when the fuel mass flow rate of the turbojet engine is decreasing to zero gradually. Existed research on mode transition control of TBCC engines usually neglect this process or simplify this process to a rapid thrust loss due to lacking a windmilling model. However, the absence of the windmilling model might cause critical problems unconsidered at the control system design stage and increase the risk of mission abortion. This paper provides a method of modeling the windmilling operation of a TBCC engine. Sub-idle compressor component maps are obtained by the interpolation method based on a zero-speed line to consider the characteristics that the total pressure ratio is below one. Friction torque is added in the torque balance relationship to achieve the free windmilling operation state when both the compressor and the turbine provide positive torques. The inlet start/unstart status coupled with the operating point of the turbojet engine is also considered. Results show that this model could reflect the characteristics that the total pressure ratio of the compressor is below one, and it provides a positive torque instead of a negative one when the turbojet engine operates at some sub-idle states. Moreover, inlet unstart would happen at some point when the turbojet engine is shutting down to the free windmilling state, under which the turbojet engine would bring in the flight drag instead of a positive thrust. In the descent mode transition when the turbojet engine is accelerating to be restarted, an inappropriate rotating speed of the inlet splitter could result in a fail inlet restart.

Generating axial compressor maps to zero speed

Gas turbine performance models typically rely on component maps to characterize engine component performance throughout the operational regime. For the sub-idle case, the lack of reliable rig test data or inability to run design codes far from design conditions entails that component maps have to be generated from the extrapolation of existing data at higher speeds. This undermines the accuracy of whole-engine sub-idle performance models, at times impacting engine development and certification of aviation engines and the accuracy of start-up performance prediction in industrial gas turbines. One of the main components driving this issue is the core compression system, which can present operability concerns during light-up and which also sets the combustor airflow required for ignition. This paper presents, discusses, and draws on previous approaches to describe a method enabling the creation of sub-idle compressor maps from analytical and physical grounds. The method relies on the calculation of zero-speed and torque-free lines to generate a map down to zero speed along with analytical interpolation. A method for the interpolation process is described. A sensitivity study is carried out to assess the effects that different elements of the map generation process may have on the accuracy of the resulting performance calculation. Overall, a method for the generation of accurate, consistent maps from limited geometry data is identified.

Infection biomarkers in assisting the judgement of blood stream infection and patient prognosis: a retrospective study incorporating principal components analysis.

BackgroundThis study aimed to evaluate whether inexpensive and quickly available infection biomarkers including procalcitonin (PCT), C-reactive protein (CRP), white blood cell (WBC) count, and percentage of neutrophils (N%) are helpful in assisting the judgement of blood culture results and patient prognosis.MethodsThis retrospective study included patients who were admitted to the intensive care unit (ICU) of Changzheng Hospital from July 2015 to June 2017 and had at least one episode of blood culture with matched infection biomarkers (PCT, CRP, WBC, and N%). Primary infection biomarkers were transformed into newly derived components using the principal component analysis (PCA) method. Each observation was plotted as a point on the component map using factor scores as coordinates. The distribution characteristics of patients with different blood culture results and prognosis were explored. The diagnostic performance of the components and infection biomarkers in the discrimination of blood culture results and patient prognosis were compared using receiver operating characteristic (ROC) curves.ResultsA total of 768 episodes of blood cultures from 436 patients were analyzed. Patients with positive blood cultures were associated with higher ICU mortality, in-hospital mortality, longer ICU stay and hospital stay (P<0.001 for all). In PCA, the 4 sets of primary infection biomarkers (PCT, CRP, WBC, and N%) were transformed into components 1 and 2. On the component map, observations of positive blood cultures were more likely to be distributed in the first and second quadrants than those of negative blood cultures (OR, 6.28, 95% CI, 4.14–9.64, P<0.001). Compared to patients with negative blood cultures, non-survivors with positive blood cultures were more likely to be distributed in the first and second quadrants (OR, 6.90, 95% CI, 2.67–20.98, P<0.001), followed by survivors with positive blood cultures (OR, 3.44, 95% CI, 1.97–6.13, P<0.001). PCT- and CRP-derived component had the largest area under curves (AUCs) in the discrimination of blood culture results (0.81) and patient prognosis (0.69).ConclusionsPCT- and CRP-derived component was more strongly associated with blood culture results and patient prognosis than WBC- and N%-derived component and primary biomarkers.

Investigation of structural proteins in sea cucumber (Apostichopus japonicus) body wall

Structural proteins play critical roles in the food quality, especially texture properties, of sea cucumbers and their products. Most of the previous studies on sea cucumbers focused on few individual proteins, which limited our understanding of how structural proteins influenced the quality of sea cucumbers. Inspired by the clarification of sea cucumber (Apostichopus japonicus) genome, we established an integrated data of structural proteins in the sea cucumber body wall. A portfolio of 2018 structural proteins was screened out from the sea cucumber annotated proteome by bioinformatics analysis. The portfolio was divided into three divisions, including extracellular matrix proteins, muscle proteins, and proteases, and further classified into 18 categories. The presence of 472 proteins in the sea cucumber body wall was confirmed by using a proteomics approach. Moreover, comparative proteomics analysis revealed the spatial distribution heterogeneity of structural proteins in the sea cucumber body wall at a molecular scale. This study suggested that future researches on sea cucumbers could be performed from an integrated perspective, which would reshape the component map of sea cucumber and provide novel insights into the understanding of how the food quality of sea cucumber was determined on a molecular level.

Additive regression with Hilbertian responses

This paper develops a foundation of methodology and theory for the estimation of structured nonparametric regression models with Hilbertian responses. Our method and theory are focused on the additive model, while the main ideas may be adapted to other structured models. For this, the notion of Bochner integration is introduced for Banach-space-valued maps as a generalization of Lebesgue integration. Several statistical properties of Bochner integrals, relevant for our method and theory and also of importance in their own right, are presented for the first time. Our theory is complete. The existence of our estimators and the convergence of a practical algorithm that evaluates the estimators are established. These results are nonasymptotic as well as asymptotic. Furthermore, it is proved that the estimators achieve the univariate rates in pointwise, $L^{2}$ and uniform convergence, and that the estimators of the component maps converge jointly in distribution to Gaussian random elements. Our numerical examples include the cases of functional, density-valued and simplex-valued responses, demonstrating the validity of our approach.

Performance Improvement of Turbofans by Electric Power Transfer

Abstract With design trends toward the more electric engine (MEE) for the more electric aircraft (MEA), novel technologies can be pinpointed for multi-spool engines. Provided that a multi-spool MEE is equipped with electrical machines connected to each of its shafts, using power electronic converters (PECs) within a common high-voltage DC bus configuration, it is possible to redistribute a desired amount of power between the engine shafts independent of their speeds. This paper presents the impact of electric power transfer (EPT) on engine performance by using a developed 0-dimensional engine model based on the inter-component volume (ICV) method and engine component maps. Generic component maps are scaled to match the design point of the CFM56-3 engine. Validating the simulation results with engine performance data from literature shows that the steady-state error of the speed and fuel consumption is within 1% and 3.5% for the high- and low-speed settings, respectively, which is acceptable for the purpose of power transfer studies. It is shown that a 400 kW EPT system is the best performing for the cases run for the CFM56-3 engine, which can halve the amount of bleed air from variable bleed valves (VBVs). Results show that EPT with the rescheduled VBVs opening improves the engine performance significantly at low-speed settings by decreasing fuel consumption and increasing surge margins. Detailed simulation results from the engine model and EPT weight penalty analysis show that fuel consumption for short- and medium-haul flights reduces by up to 0.46% and 0.79% with state-of-the-art, and 0.60% and 1.0% with future technologies, respectively. Furthermore, results show that electric power transfer can recover the surge margins of degraded engines at high-speed settings.

A new component maps correction method using variable geometric parameters

Accurate engine performance models are important for model-based performance evaluation of aero engine. The accuracy of the model often depends on engine component maps, so there is a need for a method that can accurately correct the component maps of the model over a wide range. In this paper, a new method for modifying component maps is proposed, this method combines the correction of the scaling factors with the solution process of the off-design working point, and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line, in order to obtain more correction results and guarantee high accuracy in a wider range. The method is validated by taking the main fan of the Adaptive Cycle Engine (ACE), an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft, as an example. The results show that the maximum error between the corrected component maps and the target maps is less than 1%. New possibility for more precise component maps can be realized in this paper.

An SVM-Based Scheme for Automatic Identification of Architectural Line Features and Cracks

This research investigates fundamental problems in object recognition in earthen heritage and addresses the possibility of an automatic crack detection method for rammed earth images. We propose and validate a straightforward support vector machine (SVM)-based bidirectional morphological approach to automatically generate crack and texture line maps through transforming a surface image into an intermediate representation. Rather than relying on the application of the eight connectivity rule to a combination of horizontal and vertical gradient to extract edges, we instruct an edge classifier in the form of a support vector machine from features computed on each direction separately. The model couples a bidirectional local gradient and geometrical characteristics. It constitutes of four elements: (1) bidirectional edge maps; (2) bidirectional equivalent connected component maps; (3) SVM-based classifier and (4) crack and architectural line feature map generation. Relevant details are discussed in each part. Finally, the efficiency of the proposed algorithm is verified in a set of simulations that is satisfactorily conforming to labeled data provided manually for surface images of earthen heritage.

Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens.

Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1)conventional machine learning methods and 14 texture features extracted from TCMs, (2)transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3)transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.

Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals.

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across nine functional domains, in both a discovery cohort (n = 6005) and a replication cohort (UK Biobank, n = 9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g., somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared with unimodal cortex, which may lead to higher interindividual variability and lower S-F coherence.

Departures of Rangeland Fractional Component Cover and Land Cover from Landsat-Based Ecological Potential in Wyoming, USA

Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes associated with land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the greenest and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs of soil maps and spectral data associated with the 75th percentile of the Normalized Difference Vegetation Index in the Landsat archive. We trained our EP models with 2015 component cover maps on ecologically intact sites with relatively lower bare ground than expected. We generated departure of vegetation cover by comparing the EP and 2015 fractional cover. The departures represent land cover change from potential land cover and/or within-state changes in 2015. Next, we converted EP and 2015 fractional cover maps into thematic land cover and evaluated departure to determine if it was great enough to result in land cover change. The 2015 conditions showed reduced shrub, sagebrush, litter, and perennial herbaceous cover and increased bare ground relative to EP. Known disturbances, such as energy development, fires, and vegetation treatments, are clearly visible on the departure maps, but not on EP component maps. The most frequent departure from EP land cover was shrubland conversion to grassland. Land cover departures can be explained only in small part by known disturbance, and instead are ostensibly related to climate and land management practices. These drivers result in land cover departures that broadened the ecotone between shrubland and grassland relative to EP.

Assessing and mapping maritime transportation risk based on spatial fuzzy multi-criteria decision making: A case study in the South China sea

Maritime transportation poses various risks that may lead to the loss of life and property damage. This paper presents a spatial fuzzy multi-criteria evaluation approach for assessing and mapping maritime transportation risk by combining geospatial techniques and multi-criteria decision making. Fourteen criteria in three risk component categories (hazard, vulnerability and exposure, and mitigation capacity) were selected for use and converted into spatial layers using geospatial techniques. Risk component maps were created by weighting the indices according to a fuzzy analytical hierarchy process. A risk map was then produced by integrating the hazard, vulnerability and exposure, and mitigation capacity risk component maps. The South China Sea was used to illustrate the proposed methodology. The spatial variation of risk was characterized by identifying zones of risk (i.e., very high, high, medium, low, and very low), and the results were confirmed by historical accident data. A sensitivity analysis was further conducted to examine the robustness of the results. These findings may help related authorities to develop effective mitigation plans and strategies to improve navigation safety.

Dynamic brain connectivity attuned to the complexity of relative clause sentences revealed by a single-trial analysis

To explore the issue of how the human brain processes sentences with different levels of complexity, we sought to compare the neural substrates underlying the processing of Chinese subject-extracted relative clause (SRC) and object-extracted relative clause (ORC) sentences in a trial-by-trial fashion. Previous neuroimaging studies have demonstrated that the involvement of the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) is critical for the processing of relative clause (RC) sentences. In this study, we employed independent component analysis (ICA) to decompose brain activity into a set of independent components. Then, the independent component maps were spatially normalized using a surface-based approach in order to further spatially correlate and match the equivalent components from individual participants. The selected equivalent components indicated that the LIFG and the LSTG were consistently engaged in sentence processing among the participants. Subsequently, we observed alterations in the functional coupling between the LIFG and the LSTG in response to SRCs and ORCs using a Granger causality analysis. Specifically, comprehending Chinese ORCs with a canonical word order only involved a unidirectional connection from the LIFG to the LSTG for the integration of lexical-syntactic information. On the other hand, comprehending Chinese SRCs required bi-directional connectivity between the LIFG and the LSTG to fulfill increased integration demands in reconstructing the argument hierarchy due to a non-canonical word order. Furthermore, through a single-trial analysis, the strength of the connectivity from the LIFG to the LSTG was found to be significantly correlated with the complexity of the SRC sentences as quantified by eye-tracking measures. These findings indicated that the effective connectivity from the LIFG to the LSTG played an important role in the comprehension of complex sentences and that enhanced strength of this connectivity might reflect increased integration demands and restructuring attempts during sentence processing. Taken together, the results of the present study reveal that interregional interaction in the brain network for sentence processing can be dynamically engaged in response to different levels of complexity and also shed some light on the interpretation of neuroimaging and behavioral evidence when accounting for the nature of sentence complexity during reading.