Success Ratio Research Articles

Alteration in left frontoparietal connectivity correlates with impaired cognitive reappraisal in early psychosis

BackgroundImpaired cognitive reappraisal is a notable symptom of early psychosis, but its neurobiological basis remains underexplored. We aimed to identify the underlying neurobiological mechanism of this impairment by using resting-state functional connectivity (FC) analyses focused on brain regions related to cognitive reappraisal. MethodsResting-state functional magnetic resonance images were collected from 36 first-episode psychosis (FEP) patients, 32 clinical high-risk (CHR) individuals, and 48 healthy controls (HCs). Whole-brain FC maps using seed regions associated with cognitive reappraisal were generated and compared across the FEP, CHR and HC groups. We assessed the correlation between resting-state FC, reappraisal success ratio, positive symptom severity and social functioning controlling for covariates. ResultsFEP patients showed higher FC between the left superior parietal lobe and left inferior frontal gyrus than HCs. Higher FC between the left superior parietal lobe and left inferior frontal gyrus negatively correlated with the reappraisal success ratio in the FEP group after controlling for covariates. Lower FC correlated with lower positive symptom severity and improved global functioning in the FEP group. ConclusionsAlteration in left frontoparietal connectivity reflects impaired cognitive reappraisal in early psychosis, and such alteration correlates with increased positive symptoms and decreased global functioning. These findings offer a potential path for interventions targeting newly emerging symptoms in the early stages of psychosis.

Augmented support vector regression with an autoregressive process via an iterative procedure

The Support Vector Regression (SVR) technique can approximate intricate systems by addressing learning and estimation challenges within a reproducing kernel Hilbert space, devoid of reliance on specific parameter assumptions. However, when dealing with correlated data like time series, the SVR method often falls short in accounting for underlying temporal structures, leading to limited enhancements in prediction efficiency. We introduce an enhanced SVR method that considers temporal correlations (TemporalSVR) to overcome this constraint. Our proposed method extends kernel functions to include additional linear kernels, facilitating learning temporal patterns. Additionally, we develope an iterative training procedure for the augmented regression model. During model training, we estimate the hyper-parameter in the corresponding loss function using a ‘working’ likelihood approach, enhancing the generalization capabilities of the proposed regression. To demonstrate superior forecasting performance, we conduct extensive numerical simulations on both linear and nonlinear systems and the TemporalSVR achieves improvements ranging from 8% to 114% based on the RMSE ratio from the AR-X model. Furthermore, we investigate the forecasting performance of three basic models (NARX-NN, Statistical SVR, and SVR-ARIMA) and four deep learning (DL) techniques (Transformer, Informer, Reformer, Autormer, and Autoformer) by using a WTI forecasting study. Our proposed TemporalSVR achieves the smallest RMSE at 2.22 and attains the highest success ratio of stock direction prediction at 71.30%. All these numerical results highlight the effectiveness and advantages of our TemporalSVR in handling temporal data and making accurate predictions.

Assessing Routing Algorithms for Payment Channel Networks

Payment Channel Networks (PCNs) are a promising approach to overcome scalability issues of blockchains. To achieve efficient payments in PCNs, it is necessary to route transactions between a payer and a payee. Especially in large-scale PCNs, multi-hop routing becomes necessary, since transactions need to be relayed by nodes. For this, a scalable routing algorithm is needed, which fits the individual objectives of PCN users. In this article, we study whether routing protocols from the field of Wireless Sensor Networks can be applied in PCNs. To this end, we first derive requirements for routing in PCNs, select suitable approaches, and analyze to which degrees they perform well and meet the requirements. We adapt selected protocols and evaluate them with regard to the lengths of payment paths, fees, and success ratio.

Training cattle for virtual fencing: Different approaches to determine learning success

Virtual fencing (VF) offers promising future perspectives for grazing, as it simplifies fencing through the use of GPS-coordinated VF-lines. Each animal is equipped with a VF-collar, which emits an acoustic signal when the animal approaches the VF-lines. The signal stops immediately the animal turns around but if the animal continues to move towards the VF-line, an electric pulse is emitted. The use of VF is based on the animal’s ability to learn to associate the acoustic signal with the electric pulse, and thus, avoid the electric pulse by reacting appropriately to the signal. The intention of this study was to identify if heifers are able to learn the VF-system during a 12-day-period and how successful learning can be evaluated using three different approaches: i) a reaction score; ii) collar-stored data; iii) the integrated mode change function. 16 Fleckvieh heifers were enrolled in the study and divided into two groups of eight. They were not familiar with VF prior to this study. On the first day of the study the heifers were equipped with VF-collars and the two groups were assigned to two adjacent pastures. The behaviour of four heifers per group was continuously observed by two observers and behaviours were scored according to an ethogram (2 h a.m., 2 h p.m.). We analysed changes over four phases as an indication of successful learning, by measuring (i) the behavioural reactions to acoustic signals and electric pulses, (ii) acoustic signals, electric pulses, success ratio and confidence ratio. The development and calculation of the confidence ratio is our way of weighing the success ratio against the proportion of acoustic signals. Further, (iii) we assessed the time it took for the device to shift from teach mode to operating mode, which is an internal function of the VF-collars. The device changes modes due to the animals’ 20th correct response to acoustic signals without receiving an electric pulse. We validated this function and assessed the time until the mode changes in two successive rounds (Collar restart to start round two on day eight) and found a significant difference (p<0.0001) between rounds with a faster mode change occurring in round two. All three approaches suggested successful learning. From our results, learning can be separated in a) learning to avoid the electric pulse and b) learning to interact with the acoustic signal. Therefore, combining different approaches and measuring changes over time is necessary to ensure sustained learning.

Robust visual tracking via modified Harris hawks optimization

Due to its outstanding efficiency and high precision, Harris hawks optimization (HHO for short) is suitable for solving the problem of visual target tracking under conditions of occlusion, deformation, rotation and in other complicated tracking scenes. A visual target tracker based on HHO is proposed in this study. To further promote the efficiency and stability of the standard HHO method and reduce the probability of the iteration falling into local optima and algorithm prematurity, in this study we propose an improved method called Super-HHO and apply it to visual target tracking. Compared with standard HHO, Super-HHO is superior due to its parameter optimization and updating strategy. We first optimize the random parameters of HHO via chaos theory to avoid frequent repeated exploration of the feasible region. Next, we design a nonlinear renewal strategy for the escape energy, which solves the problem in traditional HHO in which the fixed escape energy cannot accurately reflect the real hunting process of Harris hawks. Mutation strategies are also designed for the locations of the prey and the hunters to improve the optimization ability and eliminate the risk of falling into local extremes. In addition, a frame scale adjustment method model is developed to address the the issue in which the use of a size-fixed tracking frame makes it easy to include too many invalid features, which reduces the efficiency. Finally, we use the OTB2015, and VOT2018 tracking evaluation datasets, which contain hundreds of visual sequences and more than 10 complex interference scenes to conduct a qualitative analysis, a quantitative analysis and a statistical analysis of ours and other classic trackers, and to effectively test and compare the success ratio, precision and stability of each tracker. The proposed method was also compared with other classic trackers using classic large-scale benchmarks such as LaSOT and TrackingNet. Experimental data prove that ours performs well in terms of robustness, precision and efficiency.

역사왜곡 대응방안에 관한 예비적 고찰

This paper aims to develop a strategy of countermeasures against pro-Japanese history distortions on the Korean history. Korean history has long been distorted by Japanese historians and their Korean students under the influence of the Japanese imperial power in 20th century. And the intensity of this distortions, become deeper and wider in recent years, has threaten the stability of the national identity and people’s mental health.&#x0D; This article is composed of 3 chapters and introductory remarks. In the first chapter, the past salient instances of distortions made by pro-Japanese groups were over-viewed focusing on the era of the second half of the 20th century. Those instances included are War-indemnity, History-textbooks for the middle and high school students, Dok-do Island, Sex slaves in Japan imperialism, Etc.&#x0D; Second chapter introduces the countermeasures against those pro-Japanese distortions, practiced by Korean people and government. It includes government’s official letter of protest, people’s social movement organization formation and it’s protest gathering and marches. Reviewing this responses, this paper is inspected on the perspective of social movement theory.&#x0D; Third part shows the strategy of the newly constructed counter-measures against history distortions. This new counter-measure is constructed as a conceptual system, composed of 4 elements including subjects, aims, resources, and object of social movement. These elements construct a social movement action system on the base of the concerted inter-relationship of those 4 elements, named the counter-measure system against history distortions. This new countermeasure system can improve the ratio of success and achievement in the counter-measure social movements against history distortions.

Reconstructed and projected beach temperatures reveal where flatback turtles are most at risk from climate change

Nest temperature is the predominant driver of emergence success and primary sex ratios in sea turtles, with female offspring produced at higher temperatures due to temperature-dependent sex determination. However, emergence success and primary sex ratios are unfeasible to measure at scale, making methods to estimate these life-history traits from predicted sand temperatures highly desirable for long-term conservation planning in the context of climate change. To address this, we used a mechanistic microclimate model to predict hourly sand temperatures, over 32 nesting seasons since 1986, at 402 West Australian beaches supporting nesting by flatback turtles (Natator depressus). Predicted sand temperatures indicated that ∼70% of these beaches carried a 'very low' to 'intermediate' risk of subjecting embryos to thermal stress. By combining these temperature predictions with a physiological model, current and future emergence success and sex ratios were projected for ten different beaches spanning a range of thermal microclimates, under various climate change scenarios. Under recent climate conditions, emergence success averaged 76%, but declined to 63% and 37% with a 2°C and 4°C increase in air temperature, respectively. The sex ratios of hatchlings varied by location, but extremely skewed sex ratios were anticipated in a 4°C warming scenario. Our projections reveal that 'high risk' nesting beaches will regularly experience clutch failure as climate change progresses, while cooler beaches offer long-term nesting potential and require protection from additional anthropogenic impacts. These projections, covering an entire genetic stock, supply demographic data for assessing extinction risks and this method can be applied to sea turtle populations worldwide.

Hybrid learning based service migration for cost minimization with deadlines in multi-user mobile edge computing systems

Mobile edge computing, a new computing model, aims to reduce network latency and improve user experience by placing computing resources closer to the users. However, when users move away from the current service areas, the quality of services will decline. To guarantee the quality of services, service migration technique is proposed and studied. Due to the wireless resource contention among users, there is service migration decision-making coupling among multiple users. Meanwhile, making real-time service migration decisions for multiple users to minimize service cost with application deadlines remains an open challenge because of the huge state space. In this paper, we formalize the service migration problem with deadlines as a Markov decision process (MDP) and propose a hybrid learning based service migration strategy. The proposed approach integrates Monte Carlo method and DQN learning, which adopts online training without Q-values and neural network approximation to generate service migration decisions for multiple users. Experimental results demonstrate that the proposed hybrid learning based service migration strategy outperforms the existing migration strategies in terms of service success ratio and service cost.

A Comparative Study and Analysis of LoRaWAN Performance in NS3

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery

Reliability Evaluation of a Wireless Sensor Network in Terms of Network Delay and Transmission Probability for IoT Applications

Reliability is a crucial performance metric for wireless sensor networks (WSNs) because it ensures network functionality even with degraded sensor nodes and wireless connections. Due to the limited availability of power resources associated with the sensor nodes, their efficacy is reduced. In addition, parameters such as the number of neighbours, packet success rate, packet size, and node capacity have a significant impact on how sensor nodes function. Similarly, inter-node distance and signal-to-noise ratio (SNR) have the greatest influence on the functioning of wireless communications in WSNs. Before evaluating the reliability of a WSN, therefore, the node and link reliability must be computed. This paper evaluates the reliability of sensor nodes by taking into account all of the parameters previously mentioned. In addition, the lifetime of each sensor node is estimated in terms of the fractional energy consumed for each wake-up operation and the transmission of packets, respectively. Using the inter-node distance and SNR values, each wireless link’s reliability is determined. Following this, a proposed algorithm evaluates the reliability of WSN by first enumerating all minimal paths between the sensor node and the base station and then converting these minimal paths into their sum-of-disjoint-product terms. Making use of a suitable example network, every step of the proposed method is illustrated. For evaluating the reliability of WSN, the proposed method is simulated in different environments. In addition, the influence of parameters such as inter-node distance, SNR, number of neighbours, packet success ratio, and time on the reliability values of WSN is portrayed and analysed.

IfNot: An approach towards mitigating interest flooding attacks in Named Data Networking of Things

Named Data Networking (NDN) has emerged as a model to accommodate content distribution, security, and mobility. Recently, NDN has been applied to the Internet of Things (IoT), referred to as Named Data Networking of Things (NDNoT). In the rapidly evolving landscape of the NDNoT securing data transmission is paramount. The main purpose and contribution of the research presented in this paper is to safeguard the security vulnerabilities of data transmission in NDNoT. This paper specifically addresses the critical issue of interest flooding attacks in NDNoT. These attacks can disrupt network operations posing far-reaching threats to data integrity and availability. To mitigate these attacks and threats the paper introduces the IfNoT mechanism and evaluates its performance through comprehensive simulations in a realistic and recognized simulator, called Cooja. IfNot identifies the potential interest flooding attacker nodes in the NDNoT environment. It reduces the impact of the attack and the undesirable interest traffic caused by such an attack, which optimizes the network resource utilization at a maximum level. The study also explores the influence of key parameters provided by the IfNoT mechanism. Moreover, the study also identifies optimum settings for these parameters to enhance network utilization. When evaluated using various metrics, including success rate, average latency, and total interest traffic, under different conditions and parameter settings, IfNoT was able to counter the interest flooding attacks effectively. IfNoT mechanism is able to increase success ratio up to 28%, decrease average latency up to 31%, and decrease total interest traffic up to 58%.

Quantification of ternary mixture of paracetamol, chlorzoxazone and ibuprofen present in tablet dosage form using ratio subtraction spectrophotometric approaches

A sensitive and selective spectrophotometric approach comprising of successive ratio subtraction was developed for quantification and resolution of spectrum of mixture containing three components without prior separation. Three components, namely paracetamol, chlorzoxazone and ibuprofen were present in tablet dosage form. The linearity studies were carried out by recording zero order spectra and measuring absorbances at 285.0, 282.0 and 220.0 nm for paracetamol, chlorzoxazone and ibuprofen respectively. The drugs exhibited linear response in the concentration range of 6.0–18.0, 3.0–15.0 and 4.0–20.0 µg / mL for paracetamol, chlorzoxazone and ibuprofen respectively. The spectrum of paracetamol was most extended which was subsequently followed by moderately extended spectrum of chlorzoxazone and unextended spectrum of ibuprofen. The ratio spectra were manipulated successfully for quantification of paracetamol, chlorzoxazone, and ibuprofen. The developed method was validated as per ICH guidelines for the parameters of specificity, linearity, precision and accuracy. The percent recoveries for all the three drugs were in the range of 98.0–102.0 % with mean recovery of paracetamol, chlorzoxazone and ibuprofen were 99.72, 99.02 and 100.34 % respectively. Additionally the validity of the method is assured by analyzing marketed formulation.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm.

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

QUANTITATIVE ANALYSIS OF DEXTROMETHORPHAN-HBr, GUAIFENESIN AND DIPHENHYDRAMINE-HCl IN TABLET DOSAGE FORM BY SUCCESSIVE RATIO DERIVATIVE SPECTRA METHOD

Objective: This study aims to develop a spectrophotometric method with the successive ratio derivative spectra method using ethanol pro analysis solvent to obtain the results of the levels of Dextromethorphan (DEX) Hydrobromide (HBr), Guaifenesin (GUA) and, Diphenhydramine (DIF) Hydrochloric Acid (HCl) in tablet dosage form. Methods: This method is straightforward to determine the levels of DEX HBr, GUA, and DIF HCl in the wavelength range of 200-400 nm using absorption, which is calculated experimentally using spectrophotometry UV-Vis. Results: The maximum wavelengths of DEX HBr, GUA and DIF HCl were obtained at 278 nm, 273 nm, and 252 nm, respectively. The average % accuracy was obtained at 99.50% for DEX HBr, 99.91% for GUA, and 99.98% for DIF HCl in dosage forms. Conclusion: This method was successfully applied to determine the levels of DEX HBr, GUA and DIF HCl in tablet preparations and met the validation requirements.

A Hyper Heuristic Algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This paper develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nt Colony, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> hale, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ine-Cosine, and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> enry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

Priority based energy and load aware routing algorithms for SDN enabled data center network

Nowadays, to accommodate the swift expansion of cloud computing, big data, and other emerging technologies, integrating data center networks and SDN (Software Defined Network) is suggested, which also improves the network management’s convenience and flexibility. As network devices in these data centers continue to increase at a high rate, efficient route management to handle these devices’ vast amounts of data has emerged as a significant concern. This work suggests a novel admission and routing scheme considering the importance or priorities of the network flows, path energy, and routing path load. Using the SDN paradigm in DCN (Data Center Network), we first develop a Mixed Integer Linear Programming (MILP) formulation by jointly considering flow priority, path energy, and path load. The MILP formulation can maximize the number of flows as well as minimize the energy consumption and load variance in the network, or it can make a trade-off among all three. However, due to the potentially long running time, later, we introduce two greedy methods, Priority Based Energy Minimization Algorithm (PEMA) and Priority Based Evenly Load Distribution Algorithm (PEDL), where PEMA aims to maximize the flow with less energy, and PEDL focuses on maximizing the flow with reduced load variation. Finally, the developed routing strategies are implemented, and the simulation results demonstrate the out-performance of our work compared to the existing works in terms of successful flow ratio, energy savings, and load balancing.

Transformer-based 2D/3D medical image registration for X-ray to CT via anatomical features.

2D/3D medical image registration is one of the key technologies for surgical navigation systems to perform pose estimation and achieve accurate positioning, which still remains challenging. The purpose of this study is to introduce a new method for X-ray to CT 2D/3D registration and conduct a feasibility study. In this study, a 2D/3D affine registration method based on feature point detection is investigated. It combines the morphological and edge features of spinal images to accurately extract feature points from the images, and uses graph neural networks to aggregate anatomical features of different points to increase the local detail information. Meanwhile, global and positional information are extracted by the Swin Transformer. The results indicate that the proposed method has shown improvements in both accuracy and success ratio compared with other methods. The mean target registration error value reached up to 0.31mm; meanwhile, the runtime overhead was much lower, achieving an average runtime of about 0.6s. This ultimately improves the registration accuracy and efficiency, demonstrating the effectiveness of the proposed method. The proposed method can provide more comprehensive image information and shows good prospects for pose estimation and achieving accurate positioning in surgical navigation systems.

The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models

This study investigates the effectiveness of sparse regression models with their diverse specifications and the impulse indicator saturation (IIS) method in forecasting crude oil prices and their returns in China. The empirical findings showed that the IIS approach and sparse regression models are more effective than the benchmark of the no-change model. The IIS approach consistently outperformed the competing models and produced reliable results in terms of both ROS2 statistics and success ratio, as it is invariant to window size and time horizon. The results suggest that sparse regression models exhibit a notable decline in accuracy compared to the IIS approach over the long horizon. However, the selection of window size in the rolling window method plays a crucial role. Meanwhile, the sparse regression models are quite sensitive to window size; with a long horizon forecast and a small window size, the accuracy of the results is far less than with a broader window size. In contrast, the IIS method provides quite accurate forecasts regardless of window size and time horizon. The elastic smoothly clipped absolute deviation (E-SCAD) and elastic minimax concave penalty (E-MCP) remained more successful across the sparse models, possibly as a result of the high correlation among the set of predictors.

A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking

Many applications suitable for multicast transmission, such as video conferencing and live e-commerce, demand high Quality of Service (QoS) and require data delivery to be completed within specified delay constraints. Some methods have been proposed for constructing delay-constrained multicast routing based on network state. However, obtaining precise network latency can be challenging, resulting in inaccuracies in delay-constrained routing calculations and, ultimately, the inability to meet application requirements. Additionally, many methods engage in an indiscriminate exploration of potential paths in the network, causing significant message processing overhead. This paper proposes an Information-Centric Networking (ICN)-based approach for delay-constrained multicast routing. Our method dynamically constructs multicast paths from tree nodes to receivers based on real-time path status detection during the join message propagation phase. Additionally, we present a method for acquiring neighborhood state information to facilitate real-time routing decisions. To curtail indiscriminate path exploration, our approach uses the ICN Name Resolution System (NRS) to obtain and select potential optimal tree nodes. For this purpose, we design a multicast service registration and resolution mechanism using the ICN Name Resolution System (NRS). Simulation results indicate that our approach exhibits a higher success ratio and concurrently incurs lower message processing overhead than some other methods, particularly in situations with stringent delay constraints.

Monte Carlo‐based service migration under multiple constraints in mobile edge computing

AbstractMobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.