Task Cost Research Articles

Optimization for container truck routing in container terminal with multi quay cranes considering emissions policy

In container terminal, the container trucks should go by a “quay crane - container trucks -import and export block” routing, and the optimization of this routing has become the focus of this paper. This study divided the problem of routing optimization into two levels: one is a routing optimization model of single container truck which takes the lowest total cost as the target, without considering the distribution of overall transportation task; the other is a routing optimization model concerning the allocation of no-load trucks, aiming at the lowest cost of transportation task. We designed an improved Particle Swarm Optimization (PSO) algorithm to solve the two-level optimization model, and the influence of emission policy on the two-level optimization model and the influence of the number of container trucks on the transportation distance of level II in optimization model are analyzed. The results show that the increase of carbon tax will lead to the decrease of emissions in level I, but has no effect on the emissions in level II. The increase in the number of container trucks will lead to a reduction of the no-load transportation distance in level II.

Found missing semantics: Supplemental prototype network for few-shot semantic segmentation

Few-shot semantic segmentation alleviates the problem of massive data requirements and high costs in semantic segmentation tasks. By learning from support set, few-shot semantic segmentation can segment new classes. However, existing few-shot semantic segmentation methods suffer from information loss during the process of mask average pooling. To address this problem, we propose a supplemental prototype network (SPNet). The SPNet aggregates the lost information from global prototypes to create a supplemental prototype, which enhances the segmentation performance for the current class. In addition, we utilize mutual attention to enhance the similarity between the support and the query feature maps, allowing the model to better identify the target to be segmented. Finally, we introduce a Self-correcting auxiliary, which utilizes the data more effectively to improve segmentation accuracy. We conducted extensive experiments on PASCAL-5i and COCO-20i, which demonstrated the effectiveness of SPNet. And our method achieved state-of-the-art results in the 1-shot and 5-shot semantic segmentation settings.

Cell classification with phase-imaging meta-sensors.

The development of photonic technologies for machine learning is a promising avenue toward reducing the computational cost of image classification tasks. Here we investigate a convolutional neural network (CNN) where the first layer is replaced by an image sensor array consisting of recently developed angle-sensitive metasurface photodetectors. This array can visualize transparent phase objects directly by recording multiple anisotropic edge-enhanced images, analogous to the feature maps computed by the first convolutional layer of a CNN. The resulting classification performance is evaluated for a realistic task (the identification of transparent cancer cells from seven different lines) through computational-imaging simulations based on the measured angular characteristics of prototype devices. Our results show that this hybrid optoelectronic network can provide accurate classification (>90%) similar to its fully digital baseline CNN but with an order-of-magnitude reduction in the number of calculations.

Physical effort precrastination determines preference in an isometric task.

How the brain decides when to invest effort is a central question in neuroscience. When asked to walk a mile to a destination, would you choose a path with a hill at the beginning or the end? The traditional view of effort suggests we should be indifferent-all joules are equal so long as it does not interfere with accomplishing the goal. Yet, when total joules are equal across movement decisions, the brain's sensitivity to the temporal profile of effort investment remains poorly understood. Here, we sought to parse the interaction of time and physical effort by comparing subjective preferences in an isometric arm-pushing task that varied the duration and timing of high and low effort. Subjects were presented with a series of two-alternative forced choices, where they chose the force profile they would rather complete. Subjects preferred earlier physical effort (i.e., to precrastinate) but were idiosyncratic about preference for task timing. A model of subjective utility that includes physical effort costs, task costs, and independent temporal sensitivity factors described subject preferences best. Interestingly, deliberation time and response vigor covary with the same subjective utility model for preference, suggesting a utility that underlies both decision making and motor control. These results suggest physical effort costs are temporally sensitive, with earlier investment of effort preferred to later investment, and that the representation of effort is based on not only the total energy required but also when it is required to invest that energy.NEW & NOTEWORTHY We use a novel paradigm that differentiates physical effort costs, task costs, and time, where subjects choose between isometric arm-pushing tasks. Subjects prefer high physical effort earlier than later. They also decide faster and respond more vigorously the greater their preference. We find a generalizable subjective utility model that includes independent time-sensitivity of physical effort and task costs. Together, we demonstrate that subjective effort includes not only the total effort invested but also its timing.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson’s rehabilitation

BackgroundThe loss of gait automaticity is a key cause of motor deficits in Parkinson’s disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.MethodsThirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.ResultsThe turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen’s d = 0.933).ConclusionThe turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment.Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657

A useful cognitive motor dual task paradigm in prodromal and manifest Huntington’s disease

IntroductionIndividuals with Huntington's disease (HD) experience increased difficulty with balance throughout disease progression. Adding a simultaneous cognitive task to a balance assessment, referred to as a dual task (DT) paradigm, may have a deleterious effect on balance, which can be expressed in terms of a Dual Task Cost (DTC), relative to a single task (ST) condition. The aim of this study is to explore whether a cognitive-motor DT paradigm uncovers balance deficits in prodromal (Pro-HD) and manifest HD, compared to healthy adults (HA). MethodsBalance under ST and DT conditions was examined using the BTracks Balance Plate and Balance software in 30 individuals with HD, 17 individuals with Pro-HD, and 20 HA. During the DT condition, participants were simultaneously administered a version of the Paced Auditory Serial Addition Test (PASAT). DTC is calculated as the relative ratio of ST to DT, controlling for ST performance: DTC= (ST − DT)/ST x100. ResultsThe HA group performed significantly better than the HD group on both the ST and DT conditions (p < 0.01), while balance scores between the HA and the Pro-HD groups were not significantly different. The DTC scores, however, were significantly better in the HA compared to both the HD (p < 0.001) and Pro-HD (p < 0.05) groups. ConclusionOur findings indicate that the addition of a cognitive task interferes with participant's balance, reflecting real-life performance, and may have additional value for estimating transition to manifest disease, appraising fall risk, or serving as a valid outcome measure in observational and interventional trials in HD.

A Novel Method to Measure Dual Task Capacity in Young Football Players: A Preliminary Study.

When deciding to return to sport, literature usually uses evaluations based on physical abilities. But, current literature urges us to use more than physical performance evaluations. Classical dual-task testing methods do not simulate in-game loads and cannot sufficiently measure football players' dual-task capacity. The purpose of our study was to create a dual-task test that would simulate football players' in- game situations and measure their capacity. Cross-sectional study. Football Pitch. Twenty-two football players (aged 17.37±0.52 years) who played in a professional club (U19, elite league) were recruited for our study. Novel dual task test with questions containing scenarios from a football game to cognitively load players, while they are performing modified t-test. After the warm-up period, participants attended four tests in random order: juggling (foot) test, speed dribbling test, long passing test, and 'novel dual-task test'. There was no significant relationship between physical performance parameters and dual task parameters (all ps>0.05). There was a significant increase in the completion time of modified t-test when performed under dual task condition (Z=-7.568, p<0.001). The increase in completion time was found as 2.14±1.29 seconds. This duration difference was calculated as 22.79±14.58%, as dual task cost. Our test provides a new method to measure athletes' dual task capacity, which is not related to physical performance and cannot be measured with current tests. This test also showed players with lower dual task ability could not keep up their performance under dual task conditions, such as passing a ball to a team-mate when being pressed by an opponent. Players with good dual task ability could maintain 1 their performance (got affected only up to 10%); players with poor dual task ability could not maintain their performance and got affected by up to 50% (with a mean of 22.79%).

Increased Auditory Dual Task Cost During Gait Initiation in Adult Patients With Persistent Concussion Symptoms

ObjectiveTo compare dual task cost (DTC) during gait initiation (GI) between a population of patients with persistent concussion symptoms (PCS) and age-matched healthy participants. DesignCohort study. SettingUniversity research center. ParticipantsA cohort sample including 15 participants with PCS (43.9±11.7y, 73.3% female) and 23 age-matched healthy participants (42.1±10.3y, 65.2% female) as controls. InterventionsParticipants were tested on a single occasion where they performed 5 trials of single task and 5 trials of dual task GI with 12-camera motion capture and 3 force plates. Main Outcome MeasuresThe dependent variables of interest were the DTC for the center of pressure (COP) displacement and velocity during the anticipatory postural adjustment (APA) phase, the COP-center of mass (COP-COM) separation, and the response accuracy during the auditory cognitive tasks. ResultsThere were significant group differences with worse DTC for the PCS participants in anterior (A)/posterior (P) displacement (PCS, −37.5±22.1%; Control, −9.7±39.2%; P=.016, d=0.874), APA medial (M)/lateral (L) velocity (PCS, −34.8±28.8%; Control, −17.0±40.21%; P=.041, d=0.866), and the peak COP-COM separation (PCS, −7.3±6.7%; Control, 0.6±6.5%; P=.023, d=1.200). There were no significant group differences in the APA A/P velocity (PCS, −38.8±33.1%; Control, −19.8±43.9%; P=.094), APA M/L displacement (PCS, −34.8±21.8%; Control, −10.6±25.3%; P=.313), or cognitive task performance (PCS, −2.7±10.8%; Control, −0.2±4.3%; P=.321). ConclusionsPCS participants had greater (worse) DTC during both the planning and execution of the task, with large effect sizes (d>0.80). PCS participants also used a posture-second strategy whereby attentional resources were inappropriately allocated to the cognitive task. These deficits may challenge a patient's ability to complete activities of daily living and limit their functional independence.

The influence of open-skill and closed-skill sports on executive functions: a systematic review

Aims/Background Open-skill sports are reportedly more effective than closed-skill sports in improving executive functions. However, it remains unclear as to its superiority in specific components of executive functions and specific populations. This review aims to explore the degree to which specific components of executive functions are affected by different sport types, open-skill sports and closed-skill sports, among different age groups. Methods Relevant articles published from 1st January 2000 to 31st December 2023 were searched and obtained from four databases, namely EBSCO, PubMed, Web of Science, and Science Direct. These studies were selected through a carefully established article-filtering workflow, governed by a set of inclusion and exclusion criteria, and the quality assessment was carried out independently by each researcher. Results Out of the 30 studies included for this systematic review, eight studies demonstrated the beneficial impacts of open-skill sports on adults by improving three executive functions subcomponents, as compared with closed-skill sports. Six studies found that open-skill sports are conducive to inhibitory control and cognitive flexibility among children and adolescents, while four studies showed that open-skill sports greatly enhance inhibitory control in elderly individuals. Conclusion Compared with closed-skill sports, open-skill sports have a favourable impact on inhibitory control and cognitive flexibility in children, adolescents, and adults, marked by shorter response time in inhibitory control tasks, as well as shorter response time and lower switch costs in cognitive flexibility tasks. In addition, relative to closed-skill sports, open-skill sports heightens accuracy in working memory tasks among adults.

Dual-task costs in speed tasks: a comparison between elite ice hockey, open-skill and closed-skill sports athletes.

Ice hockey is a high pace sports game that requires players to integrate multiple skills. Players face perceptive, cognitive, and motor tasks concurrently; hence, players are regularly exposed to dual- or multi-task demands. Dual-tasking has been shown to lead to decreased performance in one or both performed tasks. The degree of performance reductions might be modulated by the exhaustion of cognitive resources. Literature on dual-task paradigms that combine sport-relevant elements is scarce. Therefore, a novel paradigm combining cyclical speed of the lower extremities and concurrent visuo-verbal speed reading was tested and validated. Additionally, to understand the nature of dual-task costs, the relationship between these costs and cognitive performance was assessed. We hypothesized occurrence of dual-task costs in all athletes without relationship to single task performance. Differences in dual-task cost were expected between open-skill and closed-skill sports, as well as differing expertise levels. Level of cognitive function was expected to explain some variance in dual-task cost. A total of 322 elite athletes (120 ice hockey, 165 other team sports, 37 closed-skill sports) participated in this study. Each athlete performed a tapping task, a visuo-verbal speed-reading task, and both tasks simultaneously. All ice hockey athletes performed additional cognitive tests assessing processing speed, spatial working memory, sustained attention, two choice reaction time, and motor inhibition. The results of paired-sample t-tests confirmed significant dual-task costs for all sport groups (p < 0.001). Single-task performance and dual-task costs correlated weakly in a positive direction. A one-way ANOVA revealed significantly greater costs in closed-skill sports athletes than in ice hockey and other sports athletes. No significant differences in dual-task costs were found between teams of differing expertise levels. Lastly, no significant regression model was found to predict dual-task costs from cognitive test performance. Our study suggests that this novel dual-task paradigm was successful in inducing dual-task costs for all elite athletes. Since it distinguishes between closed-skill and open-skill sports athletes, it might be a valuable diagnostic tool for performance and for talent development of open-skill athletes. Dual-task costs could not be relevantly predicted via cognitive performance measures, questioning cognitive resource theories as an explanation for dual-task costs.

Joint Optimization Algorithm for Latency-Energy and Privacy Combining Discrete Differential Evolution and Convex Optimization

A privacy protection task offloading algorithm combining discrete differential evolution and convex optimization is proposed to resolve the new position privacy leakage problem for mobile terminal due to transferring tasks to multiple edge servers for computation via wireless networks. First, the privacy cost of offloading decision is defined for the characteristics of the position privacy problem combined with the standard deviation theory. Second, the objective problem is modeled as a joint optimization of position privacy protection, offloading decision and transmission power, and the total cost of task offloading is an optimization objective. Finally, to handle the objective problem efficiently, offloading decision variable is decoupled from the transmission power variable to decrease the computational complexity of the problem; the discrete differential evolution algorithm is designed to accumulate the approximate optimal offloading decision, and the optimal transmission power is efficiently solved by variable substitution method and subgradient method. Simulations reveal that the algorithm has excellent convergence; it improves the position privacy protection level of mobile terminal and reduces the total cost of task offloading while maintaining lower energy consumption and latency.

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

AbstractTraditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.

Leveraging lightweight blockchain for secure collaborative computing in UAV Ad-Hoc Networks

Unmanned Aerial Vehicle (UAV) Ad-Hoc Networks (UANET) enable collaborative work among UAVs for versatile task execution, but they face security challenges due to physical vulnerabilities, software issues, and dynamic wireless networks. This paper proposes a secure collaborative computing framework based on blockchain technology. Specifically, we first design a lightweight blockchain scheme suitable for UANET and present an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on trust evaluation, aiming to reduce consensus overhead and establish trust relationships among UAVs. Furthermore, we devise a smart contract-based UAV task allocation strategy that considers both task execution efficiency and offloading security. This strategy enables UAVs to make optimal task-offloading decisions and facilitates collaborative computing according to smart contract rules. Simulation results demonstrate that our proposed consensus algorithm reduces average consensus latency by 47% and message count by 76% compared to PBFT-based algorithms. Additionally, the task allocation strategy decreases task costs by 48% compared to local computing strategies, achieving efficient and secure collaboration among UAVs in UANET. The real-world experiments with a UAV swarm further validate the efficiency and security of our framework, confirming its practical applicability in UANET.

Perception of task duration affects metabolic cost during split-belt adaptation.

Individuals can markedly reduce the metabolic cost of a walking adaptation task if they believe they must sustain the task for a prolonged time. The variables typically used to track adaptation do not track the reductions in metabolic cost observed in our study. Metabolic cost reduction can occur without clear changes in biomechanical factors.

Lightweight environment sensing algorithm for intelligent driving based on improved YOLOv7

AbstractAccurately and quickly detecting obstacles ahead is a prerequisite for intelligent driving. The combined detection scheme of light detection and ranging (LiDAR) and the camera is far more capable of coping with complex road conditions than a single sensor. However, immediately afterward, ensuring the real‐time performance of the sensing algorithms through a significantly increased amount of computation has become a new challenge. For this purpose, the paper introduces an improved dynamic obstacle detection algorithm based on YOLOv7 (You Only Look Once version 7) to overcome the drawbacks of slow and unstable detection of traditional methods. Concretely, Mobilenetv3 supplants the backbone network utilized in the original YOLOv7 architecture, thereby achieving a reduction in computational overhead. It integrates a specialized layer for the detection of small‐scale targets and incorporates a convolutional block attention module to enhance detection efficacy for diminutive obstacles. Furthermore, the framework adopts the Efficient Intersection over Union Loss function, which is specifically designed to mitigate the issue of mutual occlusion among detected objects. On a dataset consisting of 27,362 labelled KITTI data samples, the improved YOLOv7 algorithm achieves 92.6% mean average precision and 82 frames per second, which reduces the Model_size by 85.9% and loses only 1.5% accuracy compared with the traditional YOLOv7 algorithm. In addition, this paper builds a virtual scene to test the improved algorithm and fuses LiDAR and camera data. Experimental results conducted on a test vehicle equipped with a camera and LiDAR sensor demonstrate the effectiveness and significant performance of the method. The improved obstacle detection algorithm proposed in this research can significantly reduce the computational cost of the environment perception task, meet the requirements of real‐world applications, and is crucial for achieving safer and smarter driving.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

Deep reinforcement learning based latency-energy minimization in smart healthcare network

Significant breakthroughs in the Internet of Things (IoT) and 5G technologies have driven several smart healthcare activities, leading to a flood of computationally intensive applications in smart healthcare networks. Mobile Edge Computing (MEC) is considered as an efficient solution to provide powerful computing capabilities to latency or energy sensitive nodes. The low-latency and high-reliability requirements of healthcare application services can be met through optimal offloading and resource allocation for the computational tasks of the nodes. In this study, we established a system model consisting of two types of nodes by considering nondivisible and trade-off computational tasks between latency and energy consumption. To minimize processing cost of the system tasks, a Mixed-Integer Nonlinear Programming (MINLP) task offloading problem is proposed. Furthermore, this problem is decomposed into task offloading decisions and resource allocation problems. The resource allocation problem is solved using traditional optimization algorithms, and the offloading decision problem is solved using a deep reinforcement learning algorithm. We propose an Online Offloading based on the Deep Reinforcement Learning (OO-DRL) algorithm with parallel deep neural networks and a weight-sensitive experience replay mechanism. Simulation results show that, compared with several existing methods, our proposed algorithm can perform real-time task offloading in a smart healthcare network in dynamically varying environments and reduce the system task processing cost.

Research on Task Scheduling for Internet of Things Cloud Computing Based on Improved Chicken Swarm Optimization Algorithm

Aiming at the shortcomings of long completion time and high consumption cost of cloud computing batch task scheduling in IoT, an Improved Chicken Swarm Optimization Algorithm (ICSO) for task scheduling in cloud computing scenarios is proposed. Specifically, in order to solve the problems of slow convergence and falling into local optimum of the chicken swarm optimization algorithm, we adopt the nonlinear decreasing technique of the rooster and the weighting technique of the hen, optimize the following coefficients of the chicks, and apply ICSO to cloud computing task scheduling. In simulation experiments, we conducted a large number of experiments using four standard benchmark functions with different number of tasks and the results show that ICSO algorithm reduces 25.8%, 9.3%, 8.8%, 7.5% in small task time compared to CSO, DCSO, GCSO, ABCSO in large task time by 30.8%, 8.3%, 7.8%, 6.3%, 11.8%, 10.3%, 8.8%, 7.5% savings in small task cost and 25.8%, 11.2%, 10.8%, 9.3% savings in large task cost. This method effectively reduces task scheduling time and cost consumption. Meanwhile, we tested it in combination with an IoT-based cloud platform and achieved very satisfying Results.

The Design and Development of Delta Arm for Multi-Purpose Agribots

Many challenges and obstacles prevail in the agricultural fields, such as weed removal, horticulture, and spraying herbicides and fertilizers. Individual robots for all these tasks cost a lot, which ordinary farmers cannot afford. The delta arm has to perform versatile functions like spraying and weed removal with great accuracy and reduced environmental impact. The primary objective of this research is to design a delta arm that can move in all directions efficiently. The driving and driven arm in the delta arm design should be correlated, which helps with the proper positioning of the end effector carrier. Similarly, the moving platform and the fixed platform are designed in a circular form. These measurements give a proper design and make the robotic arm work properly as it can move in all three degrees of freedom in XYZ directions. The NVIDIA jetson orin nano operates as the controlling unit of the delta arm. The study proved that the delta arm had achieved an accuracy rate of 98% at a speed of 107.5 mm/s, making it well-suited for agricultural field operations. In addition, the delta arm exhibits the capability to handle an end effector weighing up to 300 g. Therefore, introducing the delta arm agribot in the row-based agricultural field can make a revolution in the upcoming years as it can perform versatile tasks at a high speed.

Examining sources of language production switch costs amongst Tibetan-Chinese-English trilinguals

ABSTRACT The present study tested whether the switch costs of production-based language switching tasks by trilinguals are due to competition from outside language schemas or from within language systems. We recruited 90 Tibetan-Chinese-English trilinguals in 10th grade in a Tibetan middle school in China to name digits and number words using different stimuli valence. The study was divided into three experiments, one for each combination of two languages possible amongst Tibetan, Chinese and English. We observed the switch cost symmetrical patterns in the three experiments. The results suggest that the sources of switch cost are partially from outside competition between language schemas and partially from within language system. These results may contribute to our understanding of the language control mechanism between production-based and comprehension-based language switching tasks and language control mechanism of trilinguals with three orthographically different languages.