Multi-source Data Research Articles

Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand.

Improved 3D characterization of in-situ soil desiccation cracking by multi-source data integration

Desiccation cracking is a common and natural phenomenon under a drought climate. The geometric and morphologic characteristics of the crack pattern are critical to understanding the response of soil mechanical and hydraulic properties to drought climate. It is always a big challenge to obtain the refined geometric structure of the in-situ soil desiccation crack network. This study proposes an integrated method named ERT+ for determining the three-dimensional geometry of in-situ soil desiccation crack networks by integrating multi-source data from electrical resistivity tomography (ERT), surface image analysis, and depth investigation. The proposed method was applied to three in-situ expansive soil sites with different crack geometries and soil properties. The results showed that the joint application of ERT with other investigations reduced the ambiguity of interpreting each technique independently. The crack network model characterized the three-dimensional geometric structure of the desiccation crack accurately and quantitatively, verifying the effectiveness and feasibility of the ERT+ method. Field and laboratory experiments showed that heterogeneity in soil properties resulted in different cracking morphologies (width, depth, and width-depth ratio). The crack geometric data suggested that the width-depth ratio of the crack network was related to the cracking modes and the soil properties, indicating the non-homology of different crack networks. In addition, the correction gradients calculated from the ERT+ method also varied with the cracking modes and soil properties, further suggesting the reliability and prospect of the proposed method.

Feature level fusion of multi-source data for network intrusion detection

<span lang="EN-US">The generation of data, collecting, and refining in computer networks have increased exponentially in recent years. Network attacks have also grown in prevalence with this proliferation of data and are now an inherent issue in complicated networks. Current network intrusion detection systems (NIDS) have significant issues with regard to anomaly detection. Several machine learning classification approaches are used to create NIDSs, but they are not sufficiently sophisticated to reliably detect complicated or synthetic attacks, especially if working with a lot of multi-scale data. Data fusion has been used in network intrusion detection to address these issues. For network intrusion detection, we suggested a multi-source data fusion technique in this research, which combines specific features from two datasets to produce a single dataset. Also, a machine learning classifier with fewer parameters is utilized for the fused dataset. The random forest shows the best classification accuracy compared to others in this work. For the normal classification, model accuracy is 92.8%, and the proposed fusion model showed 97.3% accuracies. Furthermore, the findings show that, when compared to other cutting-edge techniques, the suggested model is substantially more effective in detecting intrusions.</span>

Investigating the Spatiotemporal Pattern between Street Vitality in Historic Cities and Built Environments Using Multisource Data in Chaozhou, China

Investigating the Spatiotemporal Pattern between Street Vitality in Historic Cities and Built Environments Using Multisource Data in Chaozhou, China

Analysis of Urban Thermal Environment Evolution and Mechanisms Based on Multisource Data: A Case Study of Hangzhou, China

Analysis of Urban Thermal Environment Evolution and Mechanisms Based on Multisource Data: A Case Study of Hangzhou, China

Verification of the accuracy of Sentinel-1 for DEM extraction error analysis under complex terrain conditions

The successful launch of the Sentinel-1 satellite in 2014 brought a large amount of free SAR images to researchers and scholars, and its application in the fields of ocean monitoring, land use change, natural disaster monitoring and emergency response is becoming increasingly mature and precise. The main applications of InSAR can be categorized into surface deformation monitoring and DEM generation. Sentinel-1 was initially designed for surface deformation monitoring; thus, there are fewer relevant studies on the use of Sentinel-1 data for DEM extraction. However, as the only SAR satellite whose data are currently free and openly available and whose data are constantly updated, it is highly important to study its sources of error in the DEM generation process and the accuracy of its products. In addition, the SAR data provided by the Sentinel-1 satellite has the advantages of high resolution, all-day, all-weather, providing a large data source for DEM production. Taking the Ankang area as an example, this paper analyzes the influence of the InSAR spatiotemporal baseline, ground cover, terrain factors, SAR imaging and other factors on the accuracy of the Sentinel-1-extracted DEM using multisource ground observation data to validate its feasibility for terrain mapping in complex terrain. Finally, we look forward to how to effectively improve the quality of Sentinel-1 DEM products to provide guidance and a reference for subsequent research on DEM extraction using Sentinel-1 SAR images and designation of Sentinel-1 C satellite’s parameters.

Research on Grid Multi-Source Survey Data Sharing Algorithm for Cross-Professional and Cross-Departmental Operations Collaboration

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects.

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

VPA-Net: A visual perception assistance network for 3d lidar semantic segmentation

The semantic segmentation of 3D point clouds holds paramount importance in visual perception tasks of automatic driving, including obstacle avoidance, decision control, path planning, and map construction, etc. Multi-sensor fusion is a rational and pivotal technique for implementing LiDAR semantic segmentation. However, effectively fusing and utilizing multi-source data remains a challenging task. In this work, we introduce a novel visual perception-assisted point cloud segmentation network, termed VPA-Net. This network architecture employs a dual-branch design to effectively combine spatial information from point clouds and visual cues from images, thereby bolstering the performance of 3D LiDAR semantic segmentation. More specifically, the dual-branch network structure processes and fuses multi-modal data from both point clouds and RGB images. Subsequently, the intermediate features from the two branches are merged via the proposed attention-based feature fusion module. Furthermore, to address the challenge of precise boundary prediction in large-scale point cloud scene segmentation, we introduce a refinement module based on 3D sparse convolution to enhance the spatial information of the LiDAR point clouds. The effectiveness of our method is validated on SemanticKITTI and a more challenging 3D semantic segmentation dataset. Experimental results underscore significant improvement on SemanticKITTI, with our approach surpassing the state-of-the-art method, achieving a 7.1% higher mIoU than SalsaNext.

A large-scale riverbank erosion risk assessment model integrating multi-source data and explainable artificial intelligence (XAI)

The impact of riverbank erosion poses serious threat to the environment, socio-economics and human safety. Due to the extremely complex mechanisms of erosion, assessing the risk of riverbank erosion is challenging. To address this, we propose an interpretable intelligent model framework to accurately assess large-scale riverbank erosion risk. Firstly, we constructed a multi-source dataset that encompasses 29 riverbank erosion influencing factors. Subsequently, by employing an adaptive feature weighting method, a comprehensive water level factor was synthesized, unifying data dimensions. The Relief algorithm was used to identify influential features for riverbank erosion, and an adaptive feature weighting SMOTE (AFW-SMOTE) algorithm was developed to balance the riverbank erosion dataset. Additionally, an ELM and BiGRU autoencoder was designed to effectively capture and learn key information from static and dynamic features. Finally, the outputs of the two autoencoders were integrated using the XGBoost algorithm to produce riverbank erosion risk assessment results, and the risks were visualized. This model not only performs excellently across multiple evaluation metrics but also significantly surpasses 22 other machine learning models. By integrating the Shapley value method, it enhances the model’s interpretability. This provides policymakers and relevant environmental management agencies with a powerful tool to scientifically assess and manage the risk of riverbank erosion.

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests

Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

A new approach to estimate total nitrogen concentration in a seasonal lake based on multi-source data methodology

Nitrogen, a key limiter in lake eutrophication, presents serious threats to both human health and ecological balance. Despite its non-optically active nature, this study introduces an advanced retrieval approach for total nitrogen, utilizing a synthesis of multi-source data and sophisticated machine learning algorithms to markedly boost estimation precision. This innovative method integrates environmental variables, such as water temperature, depth, and flow rate with spectral reflectance, significantly enhancing the predictive accuracy of our machine learning models with high stability. The models tested, including Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN), with XGB outperforming others by achieving robust metrics: an R2 of 0.78, a Mean Absolute Error (MAE) of 0.21 mg/L, and a Mean Absolute Percentage Error (MAPE) of 16.04 %. Applying the optimized XGB model, we documented fluctuations in nitrogen concentrations within Poyang Lake across different hydrological phases in 2021, revealing the lowest nitrogen levels during the flood season and the highest in low water periods, with high concentrations at the inlets of the North Branch of the Ganjiang River and the Raohe River estuaries. Monte Carlo simulations reveal that the model is not much sensitive to input feature errors, validating its stability. The approach proposed in this study may help more precise total nitrogen retrieval in other similar lake waters.

Flexible Dual Multi-Modal Hashing for Incomplete Multi-Modal Retrieval

Multimodal hashing aims to efficiently integrate multi-source data into a unified discrete Hamming space, facilitating fast similarity searches with minimal query and storage overhead. Traditional multimodal hashing assumes that data from different sources are fully observed, an assumption that fails in real-world scenarios involving large-scale multimodal data, thereby compromising conventional methods. To address these limitations during both training and retrieval, our approach manages dual-stage data missing, occurring in both phases. In this paper, we introduce a novel framework called Flexible Dual Multimodal Hashing (FDMH), which recovers missing data at both stages by jointly leveraging low-dimensional data relations and semantic graph structural relationships in multi-source data, achieving promising performance in incomplete multimodal retrieval. We transform the original features into anchor graphs and use existing modalities to reconstruct the anchor graphs of missing modalities. Based on these anchor graphs, we perform weight-adaptive fusion in the semantic space, supervised by original semantic labels, and apply a tensor nuclear norm to enforce consistency constraints on the projection matrices across different modalities. Furthermore, our method flexibly fuses existing and recovered modalities during retrieval. We validate the effectiveness of our approach through extensive experiments on four large-scale multimodal datasets, demonstrating its robust performance in real-world dual-missing retrieval scenarios.

Stepwise Construction and Integration of Ecological Network in Resource-Based Regions: A Case Study on Liaoning Province, China

Ecological networks are an effective strategy to maintain regional ecological security. However, current research on ecological network construction in areas with large-scale resource extraction is limited. Moreover, classic ecological network construction methods do not perform satisfactorily when implemented in heavily damaged mining landscapes. Taking the example of Liaoning Province, China, a framework for stepwise renewal of ecological networks was proposed, which integrates basic ecological sources and other sources that include mining areas. The framework was based on multi-source ecological environment monitoring data, and all potential ecological sources were extracted and screened using an MSPA model and the area threshold method. Further, ecological sources were classified into two types and three levels based on the influence of abandoned mines and the characteristics of ecosystem services in the ecological sources. Ecological corridors were extracted using the MCR model. An ecological corridor optimization process based on combining the gravity model with addition and removal rules of corridors was proposed. The results indicated that the basic ecological network in Liaoning Province included 101 ecological sources and 162 ecological corridors, and the supplementary ecological network included 28 ecological sources and 67 ecological corridors. The ecological sources were divided into two types, and corridors were divided into three types. The basic ecological network exhibited a spatial distribution of discrete connections in the west and close connections in the east. Changes in ecological network topological indicators indicated that a supplementary ecological network strengthened the structural performance of the regional ecological network, expanding spatial coverage, filling hollow areas, and enriching local details of the regional ecological network. Regulation strategies were proposed for ecological sources with different connection modes. The number of ecological sources implementing restrictive development, pattern optimization, and protective development were 101, 12, and 16, respectively. This paper provides a constructing framework of ecological networks adapted for resource-based regions. This method can support decisions for the environmental governance of mines, thus contributing to a balance between resource exploitation and ecological protection in regions.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas.The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

Exploring the spatial relationship between soil quality index and soil ecosystem services driven by social-ecological factors: A peri-urban case study in central China

Urban expansion has profound impacts on soil quality in peri-urban areas. Understanding the intricate spatial relationship between soil quality index (SQI) and soil ecosystem services (SESs) in response to social-ecological factors is crucial for achieving sustainable soil utilization and management. In this study, a total of 485 soil samples were collected from different land-use types (including paddy field, dry land, orchard, and forest) in peri-urban areas of a city (Nanchang) in central China. The assessments of soil quality based on SQI and SESs were conducted using multi-source heterogeneous data. We determined the spatial relationships between SQI and six SESs using bivariate spatial autocorrelation analysis, and deciphered the role of 11 social-ecological factors in driving SQI–SES relationships across different zones. The results showed that SQI and SESs exhibited significant spatial clustering (P<0.01), despite certain variations in their distribution patterns. SQI had significant positive spatial correlations with soil organism habitat, food provision, erosion control, carbon storage, and filtering and buffering (r = 0.16–0.48, P<0.05). Two-step clustering categorized 183 villages with distinct SQI–SES relationships into four zones, i.e., soil ecological protection zone, food production core zone, soil quality enhancement zone, and soil ecological restoration zone. The impacts of various social-ecological factors on different zones were unveiled and corresponding development recommendations were presented. Results of this study facilitate the formulation of peri-urban-specific soil conservation policies and management strategies. Our work provides guidance on the improvement of soil quality and soil ecosystems, contributing to the future achievement of sustainable development goals.

Investigating the influence of morphologic and functional polycentric structures on urban heat island: A case of Chongqing, China

Polycentric urban structures are widely regarded as solutions to mitigating urban heat islands (UHI). However, few studies clarified the influences of morphological and functional polycentrism on UHI. Using Chongqing as the case study, we revealed the spatial pattern of UHI based on ECOSTRESS images and morphological/functional polycentrism based on multi-source data. Then, we quantified the spatial and nonlinear effects of polycentric structures on UHI using spatial regressions and XGBoost-SHAP methods. The results showed that polycentric structures exhibit spillover and nonlinear effects on local heat variation. Building volume alleviated UHI in the urban core, signifying a substantial shadowing effect in a mountainous setting. Population density positively but weakly affected local UHI among urban centers. Facility mixture positively affected UHI mainly in facility-abundant subcenters within the effective range between 0.5 and 1.0. As a functional factor, functional linkages exerted positive effects mainly in the urban cores where the degree centrality is larger than 1,500 while exhibiting negative correlation in the peripheral subcenters, suggesting the cooling effects by dispersing functions in the urban core. These findings help mitigate UHI by targeting measures for optimizing polycentric structures.

How CEO transformational leadership promotes firm innovation: mediating role of collective task self-efficacy

PurposeInnovative capabilities are essential for the survival and development of an organization. Previous studies have found a positive relationship between CEO transformational leadership (CEOTFL) and firm innovation. However, limited studies have endeavored to investigate the relationship between CEOTFL and firm innovation through the cognition and attitudes of the ordinary employees, despite their significant role in corporate innovation. Existing TFL literature has predominantly concentrated on “the close relationships” between leaders and their immediate subordinates, leaving the remote influence of CEOTFL on ordinary employees under-researched. Therefore, the current study aims to explain the relationship between CEOTFL and firm innovation from a micro-level perspective.Design/methodology/approachMultilevel and multi-sourced data from 1,627 employees across 145 firms was used to test the hypotheses in this study.FindingsThe results indicate that CEOTFL can enhance firm innovation by enhancing the collective task self-efficacy of regular employees. Moreover, this effect is not observed when employees are exposed to a low level of work–life balance practices.Practical implicationsThe findings suggest that CEOs can perform increased TFL to promote firm innovation. It is recommended that CEOs demonstrate their TFL not only to their direct subordinates but also to regular employees using various methods, such as participating in corporate activities with employees, delivering public speeches and sending emails to employees. Meanwhile, the HRM system should consistently align with the CEO’s leadership approach within the organization.Social implicationsThis study strengthens the importance of ordinary employees and their contribution to firm innovation.Originality/valueThe present study contributes to the literature on the micro-foundations of the relationship between CEOTFL and firm innovation by considering ordinary employee reactions as a mediator. Furthermore, this study enriches the CEOTFL literature by investigating the distant influence of CEOTFL on rank-and-file employees through the lens of social cognitive theory. Additionally, the authors expand cue consistency theory to the realms of CEO leadership and HRM literature by integrating CEOTFL and work–life balance practices into a unified model. The findings reveal the importance of coordination between CEO leadership and HRM systems within an organization. The inclusion of Chinese sample data in this study augments the cultural diversity of the sample within the CEOTFL literature.