A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay

A Hesitant Fuzzy Linguistic TODIM Method Based on a Score Function

The authors are very grateful to the editor and anonymous referees for their insightful and valuable suggestions that have led to an improved version of this paper. The work was partly supported by the National Natural Science Foundation of China (71371107, 71171187), the National Science Foundation of Shandong Province (ZR2013GM011), the Spanish National research project TIN2012-31263, Spanish Ministry of Economy and Finance Postdoctoral Training (FPDI-2013-18193) and ERDF.Hesitant fuzzy linguistic term sets (HFLTSs) are very useful for dealing with the situations in which the decision makers hesitate among several linguistic terms to assess an alternative. Some multi-criteria decision-making (MCDM) methods have been developed to deal with HFLTSs. These methods are derived under the assumption that the decision maker is completely rational and do not consider the decision maker's psychological behavior. But some studies about behavioral experiments have shown that the decision maker is bounded rational in decision processes and the behavior of the decision maker plays an important role in decision analysis. In this paper, we extend the classical TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method to solve MCDM problems dealing with HFLTSs and considering the decision maker's psychological behavior. A novel score function to compare HFLTSs more effectively is defined. This function is also used in the proposed TODIM method. Finally, a decision-making problem that concerns the evaluation and ranking of several telecommunications service providers is used to illustrate the validity and applicability of the proposed method.National Natural Science Foundation of China 71371107 71171187Natural Science Foundation of Shandong Province ZR2013GM011Spanish National research project TIN2012-31263Spanish Ministry of Economy and Finance Postdoctoral Training FPDI-2013-18193European Union (EU

Dynamic reference point method with probabilistic linguistic information based on the regret theory for public health emergency decision-making

Group emergency decision-making is an uncertain and dynamic process, in which the decision makers may be bounded rational and have a risk appetite. To depict the vague qualitative assessments, the probabilistic linguistic term sets are employed to express the perceptions of decision makers. First, considering the regret-aversion of the decision makers’ psychological characteristic, the value function and the regret-rejoice function in the regret theory are modified to adapt the probabilistic linguistic information. Second, the definition and aggregation operators of the probabilistic linguistic time variable are proposed to describe and aggregate the dynamic decision information. Third, the probabilistic linguistic models based on the dynamic reference point method and the regret theory are studied to maximise the expectation-levels of alternatives at the relative time point. The proposed method is applied to select the optimal response strategy for the outbreak of COVID-19 in China. Finally, the comparative analysis is designed to verify the applicability and reasonability of the proposed method

A bead sequence-driven deposition pattern evaluation criterion for lowering residual stresses in additive manufacturing

Deposition patterns can significantly influence the distribution and magnitude of residual stress in additively manufactured parts. Time-consuming thermal-mechanical simulations and costly experimental studies are often required to identify the optimal patterns. A simple and generic method to evaluate and optimize the deposition pattern for the purpose of minimizing residual stress is in urgent need. To overcome the shortcomings of the current practice, here we propose a novel pattern evaluation criterion. Starting from the discretization of the deposition pattern by a series of sequence numbers, we introduce two interconnected concepts. The first is called “equivalent bead sequence number” which can be physically interpreted as an index of the localized heat accumulation induced by the deposition process. Based on this point-wise “equivalent bead sequence number”, the second concept called “bead sequence number dispersion index” which can be considered as a representation of the global heat accumulation gradient, is proposed as a criterion for assessing the resulting residual stress. The temperature fields and residual stresses of a square part with six typical deposition patterns predicted by thermo-mechanical finite element simulations are used to develop and verify the proposed criterion. It is found that the “equivalent bead sequence number” of a given pattern is closely correlated to the distribution of the associated temperature and residual stress. More interestingly, both the highest equivalent and highest maximum principal residual stress of a pattern linearly increase with its corresponding value of “bead sequence number dispersion index”. Guided by this relation, two new patterns with lower residual stress are developed and evaluated. Among all the patterns considered, the so-called S pattern shows the lowest value of the “bead sequence number dispersion index” which corresponds to the lowest residual stress. The proposed sequence-driven approach provides a new candidate for real-time evaluation and optimization of the deposition pattern in additive manufacturing.publishedVersio

Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise

Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a worth pondering question. To help those traditional businesses that have no experience to dispel the clouds and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses the combination of several linguistic terms and their proportions to reveal decision information by opposite angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs) based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover, it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering the similarity among the EGRA, TODIM, and VIKOR.Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199Scientific Research Foundation of the Graduate School of Southeast University under Grant YBJJ1832FEDER Financial Support under Grant TIN2016-75850-

Complex unit lattice cell for low-emittance storage ring light source

To achieve the true diffraction-limited emittance of a storage ring light source, such as ~10 pm.rad for medium-energy electron beams, within a limited circumference, it is generally necessary to increase the number of bending magnets in a multi-bend achromat (MBA) lattice, as in the future upgrade plan of MAX IV with a 19BA replacing the current 7BA. However, this comes with extremely strong quadrupole and sextupole magnets and very limited space. The former can result in very small vacuum chambers, increasing the coupling impedance and thus enhancing the beam instabilities, and the latter can pose significant challenges in accommodating the necessary diagnostics and vacuum components. Inspired by the hybrid MBA lattice concept, in this paper we propose a new unit lattice concept called the complex unit lattice cell, which can reduce the magnet strengths and also save space. The complex unit cell is numerically studied using a simplified model. Then as an example, a 17BA lattice based on the complex unit cell concept is designed for a 3 GeV storage ring light source with a circumference of 537.6 m, which has a natural emittance of 19.3 pm.rad. This 17BA lattice is also compared with the 17BA lattice designed with conventional unit cells to showcase the benefits of the complex unit cell concept. This 17BA lattice also suggests a new type of MBA lattice, which we call the MBA lattice with semi-distributed chromatic correction

Effect of heat input on nanomechanical properties of wire-arc additive manufactured Al 4047 alloys

Heat input is one of the most important process parameters during additive manufacturing (AM). It is of great significance to understand the effect of heat input on the microstructure and nanomechanical properties, as well as the underlying mechanisms. Wire-arc additive manufactured (WAAM-ed) Al 4047 alloys under different heat inputs were produced and studied in this work. The as-manufactured Al alloys showed hypoeutectic microstructure that consisted of primary Al (α-Al) dendrite and ultrafine Al–Si eutectic. The effect of heat input on hardness and strain rate sensitivity (SRS) were investigated through nanoindentation. The nanohardness decreased with the increasing heat input, in accordance with the trend of yield strength and microhardness in the previous studies, in which the mechanism was usually explained by the grain growth model and Hall-Petch relationship. This work suggests a distinct mechanism regarding the effect of heat input on nanohardness, which is the enhanced solid solution strengthening produced by lower heat input. In addition, the heat input had little effect on the SRS and activation volume. It is hoped that this study leads to new insights into the understanding of the relation between heat input and nanomechanical properties, and further benefits to improve the targeted mechanical properties and engineering applications of the AM-ed materials.publishedVersio

MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation

In recent years, the Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks. However, SAM does not exhibit satisfactory performance in the realm of medical image analysis. In this study, we introduce the first study on adapting SAM on video segmentation, called MediViSTA-SAM, a novel approach designed for medical video segmentation. Given video data, MediViSTA, spatio-temporal adapter captures long and short range temporal attention with cross-frame attention mechanism effectively constraining it to consider the immediately preceding video frame as a reference, while also considering spatial information effectively. Additionally, it incorporates multi-scale fusion by employing a U-shaped encoder and a modified mask decoder to handle objects of varying sizes. To evaluate our approach, extensive experiments were conducted using state-of-the-art (SOTA) methods, assessing its generalization abilities on multi-vendor in-house echocardiography datasets. The results highlight the accuracy and effectiveness of our network in medical video segmentation

Quantum key distribution and beyond: introduction

This feature issue presents a collection of recent theoretical and experimental developments in the field of quantum key distribution (QKD) and its extension to other quantum cryptography protocols and devices. It encompasses work on a variety of QKD protocols, including continuous-variable, measurement-device independent, and twin-field QKD, as well as other newly proposed protocols, in platforms ranging from optical fiber through to wireless indoor and satellite links. It covers examples of hacking strategies and their countermeasures as well as applications of machine learning techniques in designing quantum networks. It also includes new developments in efficient superconducting photon-number resolving detectors as well as fast quantum random number generators. Distinctively, this feature issue demonstrates how different expertise in science and engineering can come together to produce an outcome that hopefully takes us one step closer to the wide-scale deployment of quantum communications technologies