Simulation Experiments Research Articles

How does idea rejection translate into employee creativity: A perspective from incubation theory

Previous studies have generally posited that idea rejection negatively impacts innovation performance, however, few have explored the potential positive effects. From a novel perspective grounded in incubation theory, this research investigated whether, how, and when idea rejection could be translated into employee creativity. Six studies were conducted: a questionnaire survey (Study 1a) and five simulated situational experiments (Studies 1b, 1c, 1d, 2, and 3). In Study 1a, we collected samples from various industry backgrounds, including manufacturing, high-tech companies, internet-related companies, and modern service industries, to verify whether idea rejection could be translated into employee creativity. Studies 1b, 1c, and 1d further validated the findings of Study 1a in distinct fields—new home and kitchen products, technology, and scientific research—through simulated situational experiments. Studies 2 and 3 selected new home and kitchen products as representative experimental situations and used simulated situational experiments to explore the mechanisms and boundary conditions through which idea rejection translates into employee creativity. The results revealed that idea rejection could be translated into employee creativity. This translation was mediated by both problem restructuring and the activation of new knowledge. Furthermore, servant leadership positively moderated the relationship between idea rejection and the activation of new knowledge, thereby strengthening the mediating role of the latter. However, servant leadership did not moderate the relationship between idea rejection and problem restructuring, nor did it enhance the mediating role of problem restructuring. These findings opened the “black box” of how idea rejection is translated into employee creativity, expanded the research on the effects of idea rejection, and provided a reference for organizational innovation management practices.

Automated identification of impact spatters and fly spots with a residual neural network.

In criminal investigations, distinguishing between impact spatters and fly spots presents a challenge due to their morphological similarities. Traditional methods of bloodstain pattern analysis (BPA) rely significantly on the expertise of professional examiners, which can result in limitations including low identification efficiency, high misjudgment rates, and susceptibility to external disturbances. To enhance the accuracy and scientific rigor of identifying impact spatters and fly spots, this study employed artificial intelligence techniques in image recognition and transfer learning. Two types of bloodstains obtained from simulation experiments were utilized as datasets, and a pre-trained neural network, ResNet-18, was employed for feature extraction. The original fully connected layer was replaced, and a new fully connected layer with a dimensionality of 2 was introduced to fulfil the task requirements. The results demonstrate that the transfer learning network model, based on ResNet-18, achieved a maximum accuracy of 93 % in morphologically identifying impact spatters and fly spots. The objective is to assist crime scene investigators and BPA analysts to identify bloodstains at homicide scenes conveniently, rapidly and accurately, thereby furnishing scientific evidence for scene reconstruction and advancing BPA toward intelligent practices.

On the cluster of the families of hybrid polynomial kernels in kernel density estimation

This study introduces a novel cluster of hybrid polynomial kernel families, designed to achieve significantly lower asymptotic mean integrated squared error compared to traditional kernels. These hybrid kernels are developed by heuristically combining classical polynomial kernels using probability axioms. An in-depth analysis of error propagation within these kernels is conducted, utilizing both simulation experiments and real-life datasets, including the Life Span of Batteries and COVID-19 datasets. The findings consistently demonstrate that the proposed hybrid kernels outperform their classical counterparts in various density estimation tasks across different distribution types and sample sizes. This research highlights the potential of hybrid polynomial kernels to enhance accuracy in density estimation, advocating for their adoption in statistical modelling and analysis.

Rapid and quantitative CEST-MRI sequence using water presaturation.

Despite the significant potential for in vivo metabolic imaging in preclinical and clinical applications, CEST MRI suffers from long scan time and inaccurate quantification. This study aims to suppress the contaminations among signals under different frequencies, which could shorten the TR and thereby facilitate CEST imaging acceleration and quantification. A novel sequence is proposed by applying a water-presaturation (WPS) module at the beginning of each TR. WPS CEST quickly knocks down the residual signal from previous TRs so that the magnetization of all TRs recovers from zero, which aligns well with the formula of quasi-steady-state theorem and enables accurate quantification within shorter TR. WPS CEST was assessed by simulations, creatine phantom, and healthy human brain scans at 3 T. In simulation and phantom experiment, WPS CEST allows accurate estimation of exchange rate (ksw) using omega plot and using shorter delay time (Td) and saturation time (Ts) (e.g., 1 s/1 s) compared with the conventional CEST. Simulations further showed that WPS CEST could obtain consistent spin-lock relaxation (R1ρ) values over varied Tds and Tss. Six human scans indicated that R1ρ collected from conventional sequences showed significant differences between two groups with Td and Ts of (1 s/1 s) and (2 s/2 s) (amide: 1.721 ± 0.051 s-1 vs. 1.622 ± 0.050 s-1, p = 0.001; nuclear Overhauser enhancement: 1.792 ± 0.046 s-1 vs. 1.687 ± 0.053 s-1, p = 0.004), whereas WPS CEST scans using these 2 Td/Ts values obtained the same mean R1ρ (amide: 1.616 ± 0.053 s-1 vs. 1.616 ± 0.048 s-1, p = 0.862; nuclear Overhauser enhancement: 1.688 ± 0.064 s-1 vs. 1.684 ± 0.054 s-1, p = 0.544). WPS CEST demonstrated accurate quantitation within shorter TR compared with conventional sequences, and thereby may allow rapid quantitative CEST scans in various situations.

Collaboratively removal of phosphate and glyphosate from wastewater by a macroscopic Zr-SA/Ce-UIO-66 adsorbent: Performance, mechanisms and applicability

Dissolved inorganic and organic phosphorus is a major factor in triggering the eutrophication of water bodies. At present, a novel Zr4+ cross-linked sodium alginate encapsulated in Ce-UIO-66 microspheres (Zr-SA/Ce-UIO-66) was prepared and systematically characterized. Its ability for capture of phosphate and glyphosate in their single and binary systems has been investigated comprehensively. Results showed that Zr-SA/Ce-UIO-66 exhibits excellent phosphate adsorption, achieving 92% removal and a maximum adsorption capacity of 125mgP/g at 313K. Diversified mechanisms, including electrostatic attraction, ligand exchange and hydrogen bonding, have cooperatively participated in phosphate removal. Interestingly, in phosphate and glyphosate mixed solutions, the presence of phosphate significantly enhanced the removal of glyphosate for the formation of complexes between phosphate ions and the adsorbent. And that was similar to the presence of glyphosate in phosphate adsorption. Simulated wastewater experiments demonstrated the adsorbent’s practical application in water contaminated with both organic phosphorus and glyphosate composites and its potential for recycling and reuse.

Influence mechanism of AC electric field on rime ice accretion on the insulator and its experimental verification in the natural environment

Insulator icing can easily trigger flashover trips in heavily ice-covered areas, and analyzing the formation mechanism of ice accumulation on the insulator is essential for predicting its flashover development. To accurately dissect the process of insulator icing, a coupled mathematical model of the flow field and electric field, as well as a droplet motion model were elaborated in this paper based on the principles of fluid mechanics and electromagnetic field. Subsequently, the characteristics of droplet motion deviation and its physical collision process with an insulator surface under AC electric field were investigated. The results demonstrate that the charged droplets tend to oscillate along the electric field lines in AC electric field, and the amplitude of the oscillation increases with the applied voltage increasing. Moreover, the number of droplets captured by the insulator rises with the increase of voltage, wind speed, and median volume diameter of the droplet. Combined with the simulation and natural icing experiments, it is observed that ice branches grow in the direction of electric field force, and as the voltage rises, ice branches gradually spread from the edge of the shed to its surface, increasing surface roughness. The density of ice exhibits an inverted U-shaped relationship as the electric field strength increases. Ice mass and ice length present nonlinear growth with an increase in icing time. Furthermore, compared with that without energization, the icing amount and icing length increase by more than 13 % under AC voltage of 40 kV.

Unfolding bovine serum albumin decorated selenium nanoparticles crosslinking with chitosan: Achieve stabilization of Pickering emulsions gel and enhance resveratrol bioaccessibility.

Resveratrol (Res) is a natural polyphenol exhibiting anti-oxidant and anti-inflammatory activity. However, the applications of Res have been limited due to its low stability and water solubility. To enhance the bioaccessibility of Res, unfolding bovine serum albumin-modified selenium nanoparticles (UBSA@SeNPs) encapsulated within chitosan (CS)-coated Pickering emulsions (CS-UBSA@SeNPs-PE) were used to load Res. The results showed that Res-loaded CS(0.06%)-UBSA@SeNPs-PE has small droplet size (16.13μm), high gel properties and excellent antioxidant properties. During the simulated digestion process, CS reduced the release rate of Res from Res-loaded CS(0.06%)-UBSA@SeNPs-PE (42.27%) to reach a slow release effect. Importantly, Res could quickly release from CS-UBSA@SeNPs-PE within intestinal fluid or in the presence of chitosanase. In simulated absorption experiments, the intestinal permeability of Res in Res-loaded CS(0.06%)-UBSA@SeNPs-PE were enhanced by 292.31% compare to Res-loaded CS(0%)-UBSA@SeNPs-PE. In pharmacokinetic studies, Res-loaded CS(0.06%)-UBSA@SeNPs-PE had an area under the curve (AUC) up to 3467.99±127.43ng*h/mL. Furthermore, CS also improved the mucoadhesive nature of UBSA@SeNPs-PE, resulting in a gut-retention time of Res-loaded CS(0.06%)-UBSA@SeNPs-PE that reached up to 60h. In conclusion, CS-UBSA@SeNPs-PE can serve as an effective oral delivery system for improving the bioaccessibility of Res.

Multidimensional RF pulse design with consideration of concomitant field effects.

To develop a small-tip multidimensional RF pulse design procedure that incorporates linear time-invariant gradient imperfections and concomitant field effects. This could be particularly important for contemporary low-field MRI systems with high-performance gradients. We developed an extension of the small-tip excitation k-space formalism, where concomitant fields were approximated as a Bloch-Siegert shift in the rotating frame. This was evaluated using realistic simulations of 2D selective excitation at various field strengths (0.2T, 0.55T, 1.5T, 3T, and 7T) with single and parallel transmit. Simulated excitation profiles from the original and extended k-space formalisms were compared. Experimental validations were performed at 0.55T with a single-channel transmit. The extended formalism provides improved 2D excitation profiles in all scenarios simulated, compared against the original formalism. The proposed method corrects the concomitant field effects on 2D selective excitations for B0 > 0.2T when the magnitude of the B0 is far larger than that of nonrotating concomitant fields. Simulation and phantom experiments at 0.55T match well for both original and proposed methods, with the proposed method providing sharper and more accurate excitation profiles at off-isocenter distances up to 15 cm. The impact of the proposed method is greatest in scenarios where concomitant fields are substantial, such as low field strengths and off-isocenter. Concomitant fields can be modeled as a Bloch-Siegert shift in the rotating frame during multidimensional RF pulse design, resulting in improved excitation profiles with sharp edges. This is important to consider for off-isocenter excitations and imaging at low field strengths with strong gradients.

The difference of organophosphate esters (OPEs) uptake, translocation and accumulation mechanism between four varieties in Poaceae.

To explore variation patterns of uptake, translocation, and accumulation processes responding to organophosphate esters (OPEs) among Poaceae plants, hydroponic and computer simulation experiments were executed. Plant growth, OPEs' concentration, and bioinformation and transcript of lipid transporters in the three terrestrial barley, wheat, and maize and aquatic rice seedlings were studied after exposure to seven OPE congeners. Four types of plants could accumulate seven OPE congeners. OPEs could promote rice growth by upregulating IAA27 hormone gene. However, maize growth was inhibited due to upregulating IAA17 hormone gene. In general, OPEs with log Kow > 4 tended to accumulate in roots of the four types of plants. Furthermore, the uptake, translocation, and accumulation mechanism of OPEs in different plants showed species-specific, depending on chemical properties of OPEs and biological factors specifically referring to the binding ability and gene expression of lipid transporters. The uptake and accumulation of OPE in aquatic rice roots were mainly influenced by biological factors. On the contrary, terrestrial plants relied on log Kow more than biological factors. Meanwhile, TIL of Poaceae plants could be a common and key protein that contributed to OPEs accumulation.

Driving against the clock: Investigating the impacts of time pressure on taxi and non-professional drivers’ safety and compliance

Drivers often encounter time pressure, which can lead to riskier driving habits, decreased safety margins, and a higher chance of accidents. Given that taxi drivers frequently experience these conditions, this study examines how time pressure impacts the driving behaviors of both taxi and non-professional drivers. In this regard, a driving simulator experiment was carried out to assess the driving behaviors of both groups under time pressure. The simulation drive included different scenarios such as a stop sign, a pedestrian crosswalk, four intersections, a school zone, a slow bus and a drop-off location. The study recruited 55 taxi drivers and 55 non-professional drivers to take part in the experiment. Each participant completed the simulation drive twice, with the second drive conducted after they were informed that a reward would be given for finishing the trip more quickly. The findings reveal that drivers exhibited significantly riskier behaviors under time pressure, including higher speeds and reduced adherence to traffic rules. When comparing both groups, non-professional drivers displayed higher speeds and riskier behaviors across various scenarios, whereas taxi drivers were more likely to commit violations associated with drop-offs. These findings call for targeted awareness campaigns and stricter enforcement to reduce risky behaviors under time pressure. Flexible scheduling for non-professional drivers and incentive programs for taxi drivers can further promote safer practices. Policymakers can use these insights to design strategies that address the risks associated with time pressure.

General decay anti-synchronization of coupled delayed memristive neural networks with constant and time-varying distributed-delay coupling

In this study, we investigate the general decay anti-synchronization (GDAS) problem for coupled delayed memristive neural networks (CDMNNs) and delayed CDMNNs (DCDMNNs) with constant and time-varying distributed-delay coupling, respectively. First, we introduce the concept of GDAS to the considered network on the basis of the definitions of ψ-type stability and the ψ-type function. Furthermore, several criteria are inferred for the GDAS of CDMNNs with constant and time-varying distributed-delay coupling by selecting the correct Lyapunov functionals and devising an appropriate nonlinear controller. Additionally, several sufficient conditions for realising GDAS for DCDMNNs with constant and time-varying distributed-delay coupling are obtained. Finally, simulation experiments demonstrate the feasibility of the obtained conclusions.

Degradation of floor finishing materials owing to continuous gait: A comprehensive study on the generation and impact of microplastics

With the increasing concern regarding the effects of microplastics (MPs) on human health, it is essential to conduct research on the MPs generated in the atmosphere and on those occurring in indoor environments. One of the causes of particle generation in indoor spaces is the degradation of floor finishing materials, including human gait factor. Therefore, gait simulation experiments were conducted to verify the occurrence of MPs during the gait process. After the experiments, the concentrations of MPs were calculated, and the differences in the concentrations under various gait and floor finishing material conditions were analyzed. Moreover, the morphological analysis of MPs indicates the mechanisms of MPs generation. Additionally, a chemical bond analysis of MPs and surface analysis of floor finishing materials demonstrated the types and sources of MPs generation. Furthermore, the health risk assessment results showed that the health effect of MPs was the lowest in the case of marmoleum flooring, approximately 1.27 and 5.77 times lower than that of laminate flooring and carpet tiles, respectively. Consequently, the study demonstrated that MPs are generated owing to the surface degradation caused by gait and also provided insights into which floor finishing material is least affected by MPs.

PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks

The identification of influential nodes, a critical problem in the field of complex networks, has been extensively studied. However, previous research has primarily focused on maximizing terminal influence across all network structures indiscriminately, making it challenging to accurately identify influential nodes in specific structures. Moreover, overlooking the influence of the temporal dynamics of propagation significantly diminishes the benefits of identifying influential nodes. Therefore, we propose an influential nodes identification model, Preference Path-based Early-stage Influence Accumulation Model (PPEIM), tailored for typical locally dense multi-core networks. The key idea of PPEIM is to identify more influential nodes in early-stage propagation by aggregating dynamic influence propagation volumes superimposed on multiple paths. Specifically, early-stage influence performance is enhanced by sampling paths, mitigating the risk of dense influential nodes resulting from redundant relationships. Moreover, the K-shell, degree, influence distance and link direction are integrated to define connection strength between nodes to guide path selection. And the concept of influence propagation volume is introduced to accurately simulate the influence residuals and losses during the propagation process. To validate the effectiveness and superiority of PPEIM in locally dense multi-core networks, five sets of simulation experiments are conducted on seven real-world datasets. Experimental results demonstrate that PPEIM outperforms six state-of-the-art methods in overall propagation capability, early-stage influence capability, disintegration capability, node dispersion, and discrimination capability.

A multivariable adaptive control method for aeroengine with H∞ performance considering engine output limitation protection based on fully adjustable Neural Network

Aeroengines, being highly nonlinear systems subject to external disturbances, present a challenge for conventional control methods in achieving satisfactory performance. The radial basis function neural networks possess the capability to effectively model highly approximate nonlinear functions, making them suitable for implementation in aeroengine control. However, the radial basis function (RBF) neural network is a local approximation model that inadequately incorporates historical information. Additionally, the tracking error of neural networks can also impact control effectiveness. To address this issue, this paper proposes a multivariable control method for aeroengines with H∞ performance based on fully adjustable Long Short-term Memory-Radial Basis Function Neural Network (LSTM-RBFNN). Firstly, a factual mathematical model of an aeroengine is established based on the principles of aerodynamics and thermodynamics. Secondly, the paper proposed an LSTM-RBF neural network architecture to enhance prediction performance and proposed using a finite time perturbation estimator to gauge the approximate error of neural networks, thereby acquiring immeasurable approximative error information. Then, in order to mitigate the influence of neural network tracking error on the loop, the estimated errors are incorporated into the primary loop controller, and a static state disturbance full feedback H∞ controller is proposed. Then, leveraging the LSTM-RBF architecture proposed in this study, we further employ Taylor expansion of nodes to derive the adaptive law of the neural network and propose a novel neural network adaptive law that incorporates tracking error estimated by the disturbance observer into the adjustment mechanism to enhance tracking performance. The stability of this adaptive law has been proven based on Lyapunov function analysis. And considering the output limitation protection problem of aeroengine, a virtual command value based limitation protection method is proposed for the above control method, and it is proved that this limitation method does not damage the stability and design process of the original control method. Finally, numerical simulation and hardware in loop simulation experiments were conducted, and the results showed that under the same adaptive gain of the neural network, the performance indicators (IAE, IATE, σ) could be reduced by more than 50% compared to other methods, which demonstrates superior tracking and disturbance rejection performance achieved by our proposed method.

A Cyber-War Between Bots: Cognitive Attackers are More Challenging for Defenders than Strategic Attackers

Adversary emulation is commonly used to test cyber-defense performance against known threats to organizations. However, many adversary emulation methods often rely on automated planning and underplay the role of human cognition. Consequently, defenders are often underprepared for human attackers who can think creatively and adapt their strategies. In this paper, we propose the design of adversarial cognitive agents that are dynamic, adaptable, and able to learn from experience. These cognitive agents are built based on the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks, making them more challenging than strategically optimal adversaries for human defenders. Our research offers three main contributions. First, in a simulation experiment, we demonstrate how IBL attacker agents can learn from experience and become as efficient as optimal strategic algorithms against a strategic defender. In a second simulation experiment, the IBL attackers are pitted against an IBL defender, showing that the IBL attacker can be a more challenging adversary for the IBL defender, while the IBL defender can learn to counter carefully crafted optimal attack strategies. To test these observations, we conducted a third experiment, where humans played the role of defenders against both strategic and IBL attackers in an interactive task. The results confirm the predictions of the second simulation experiment: a cognitive attackers are more challenging for human defenders than strategic attackers. These insights contribute to informing future adversary emulation efforts and training of cyber defenders.

A framework for parameters planning in hybrid milling of flexible blades

Abstract In milling of flexible workpieces, like thin-wall blades, cutting force induced deformation and vibration are great adverse issues which are almost inevitable. Semifinish-finish (SF) hybrid milling is a promising strategy to obtain a high-accuracy and quality surface, which separates the object blade into several layers in the radial direction of the blisk, and then for each layer, from blade tip to root, using SF hybrid milling as a cycle to finish the current layer, and then move to the next layer. However, two extra contradictions are intruduced in process planning: (1) Considering the number of layers: To decrease the machining deformation error, we should increase the number of layers, which however increases the time consumption because of frequent tool path switching between semifinishing and finishing; (2) As to the allowance of semifinishing: To decrease the machining deformation error, we should increase the allowance in semifinishing to enhance stiffness, which however increases tool wear sicne more matearial needs to be removed by the tool. To balance the contradictions, this paper constructs a framework for parameters planning for SF hybrid milling, in which the allowance, number of layers as well as the length of each layer are optimized so that we are able to control the deformation error while maintaining high cutting efficiency. The method is verified by simulation and validation experiments. Compared with traditional non-layering and uniform layering machining, the maximum deformation error by optimized layering machining is reduced by 76.4% and 48.6%, respectively.

Design of new chaotic system with multi-scroll attractor by using variable transformation and its application

Abstract Multi-scroll chaos exhibits complex dynamic behavior, the method of designing chaotic systems with multi-scroll attractor is an important research topic. Without any theoretical guidance, it is very difficult to obtain a new chaotic system, especially the multi-scroll chaotic system. In this paper, a class of new chaotic systems with n-scroll attractor is investigated by using variable transformation based on nth order polynomial of complex number. The Lyapunov exponent, bifurcation diagram, and topological horseshoe in the Poincaré cross-section of new chaotic systems are presented to rigorously prove the existence of chaos in the multi-scroll chaotic system with the assistance of computer simulation. Based on the proposed multi-scroll chaotic system, a new encryption algorithm is proposed for image information, and the simulation experiments verify the feasibility and effectiveness of the proposed encryption algorithm.

Distributed planning method for detection array of UAV formation with bearing-only sensor network

Abstract Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring

Abstract. Quantifying facility-level methane emission rates using satellites with fine spatial resolution has recently gained significant attention. However, the prevailing quantification algorithms usually require the methane column plume from a solitary point source as input. Such approaches are challenged with overlapping plumes from multiple point sources. To address these challenges, we propose a separation approach based on a heuristic optimization and the multi-source Gaussian plume model to separate the overlapping plumes. Subsequently, the integrated mass enhancement (IME) model is applied to accurately quantify emission rates. To validate the proposed method, observation system simulation experiments (OSSEs) of various scenarios are performed. The result shows that plume overlapping exacerbates the quantifying error of the IME method when applied without such a separation approach, where the quantification mean absolute percentage error (MAPE) increased from 0.15 to 0.83, and it is affected by factors such as source intervals, wind direction, and interference emission rates. By contrast, the application of the proposed separation method together with the IME quantification approach mitigates this interference, reducing the quantification MAPE from 0.83 to 0.38. Moreover, the proposed method also outperforms the direct use of multi-source Gaussian plume fitting for the quantification, with a MAPE of 0.45. Our separation method separates overlapping plumes from multiple sources into distinct, single-source observations, enabling the IME algorithm – a high-precision quantification approach for fine-spatial-resolution plume images – to handle multi-source scenarios effectively. This method can help future spaceborne carbon inventory activities for spatially clustering carbon-emitting facilities.

Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility.

The ecology of watersheds plays an important role in regulating regional climate and human activities. The sediment-soil system in the middle and lower reaches of the Yellow River Basin (Henan section) was explored. The spatial distribution characteristics of heavy metals (HMs) showed that tributaries, which are affected by anthropogenic activities, contain higher concentrations of HMs than the main channel. Sequential extraction experiments indicated that Cd had the strongest potential to be released, followed by Mn. In vitro simulation experiments showed that gastric and pulmonary fluids rendered these two orders of magnitude more bioaccessible compared to sweat. Moreover, Cd exhibited the highest bioaccessibility in both gastric and lung fluids. When bioaccessibility was considered in the evaluation of health risks, more than 82 % of reductions in non-carcinogenic and carcinogenic risk indices were observed in children and adults. A positive matrix factorization model was utilized to determine the potential sources of HMs: industrial sources, natural sources, and mixed agricultural and transportation sources were identified as the three main sources of HMs in sediments and soils. In addition, mining activities were also an HMs source in sediments.