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The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems., This work is a JSAC invited survey & tutorial paper. Copyright may be transferred without notice, after which this version may no longer be accessible

The de Winter electrocardiography (ECG) pattern is a sign that implies proximal left anterior descending coronary artery occlusion in patients with chest pain. We report a case of a 34‐year‐old man with a history of smoking who presented to the local emergency department with a 49 min history of chest pain. The first ECG of the patient indicated that ST‐segment elevation was noted in the lead V2–V4; 57 min later, a second ECG revealed a typical de Winter syndrome when the patient was transferred to the emergency chest pain centre of our hospital. A percutaneous coronary intervention (PCI) was performed approximately 8 h later because the patient initially refused the PCI. Acute coronary artery angiography showed that the proximal left anterior descending coronary artery was completely occluded. Our case suggests that ST‐segment elevation myocardial infarction may evolve in the direction of de Winter, which reflects a coronary thrombus in formation, so the de Winter ECG pattern should not be considered static.

With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service composition. However, current service composition approaches applied in the internet cannot work well in IoT environments due to weak adaptability, low accuracy, and poor time performance. This paper, based on service dependency graph, proposes a top-k QoS-optimal service composition approach suitable for IoT. It aims to construct the relationship between services by applying the service dependency model and to reduce the traversal space through effective filtering strategies. On the basis of a composition path traversal sequence, the generated service composition can be represented directly to avoid backtracking search. Meanwhile, the redundant services can be removed from the service composition with the help of dynamic programming. Experiments show that the approach can obtain the top-k QoS-optimal service composition and better time performance.

Automated maritime object detection is a significant research challenge in intelligent marine surveillance systems for the Internet of Things (IoT) and smart ocean applications. In particular, ship detection is recognized as one of the core research issues of these IoT-driven intelligent marine surveillance systems. Traditional methods based on machine learning have made some achievements in detection tasks for specific objects. However, the ship objects are relatively small, and they are usually not accurately detected. In this article, we propose an electroencephalography (EEG)-based maritime object detection algorithm for IoT-driven surveillance systems in the smart ocean. For this purpose, we conduct experiments to record the EEG signals of subjects when they are watching the maritime image scenes. With the feature analysis of EEG signals, the event-related potential (ERP) components associated with detecting objects are induced, such as the $P3$ and $N2$ components. Employing classification based on linear discriminant analysis (LDA), the area under curve (AUC) of the receiver operating characteristic (ROC) is used to evaluate the detection accuracy. We use this novel method to determine and identify essential objects and areas from IoT devices, such as digital camera imaging sensors. Our proposed method can not only help to detect small objects accurately using fewer samples but can also be used to reduce the data volume needed to be stored and transmitted in IoT-driven marine surveillance systems.

Summary: Background: The end plate plays an important role in intervertebral disc degeneration progression. The aim of the study was to examine the compositional and structural changes of the end plate with age and to investigate the correlation between end plate and disc degeneration by T1ρ and T2 map magnetic resonance imaging. Methods: There were 12 young monkeys (6-7 years old), 20 aged monkeys (14-17 years old) and 12 human participants (30-50 years old) in this study. T1ρ or T2 map values of the nucleus pulposus and end plate cartilage were analyzed according to Pfirrmann grades and age. Afterwards, micro computed tomography and histological analysis were used to confirm the end plate changes in monkeys. Pearson’s correlation was performed to investigate the relationship between end plate and disc degeneration. Results: In monkeys, T1ρ (r=-0.794, P

Background & aims Whether nonalcoholic fatty liver disease (NAFLD) is associated with risk of incident atrial fibrillation (AF) independent of established cardiovascular risk factors remains controversial. We aimed to provide a quantitative estimate of the association between NAFLD and risk of AF after adjustment for cardiometabolic risk factors. Methods In this study, we searched PubMed and Embase for studies published from database inception until January 31, 2020. Cohort studies reported adjusted relative risks (RRs) and 95% confidence intervals (CIs) for AF of NAFLD compared with non-NAFLD were included for analysis. Results A total of 6 cohort studies were included, comprising 614 673 individuals for analysis. The median follow-up duration was 10.0 years with 7271 cases of incident AF. Compared with non-NAFLD, minimally adjusted models without adjustment for cardiometabolic risk factors showed that NAFLD was associated with increased risk of AF (RR 1.65, 95% CI 1.23-2.20, I2 = 63.0%). After adjustment for multiple cardiometabolic risk factors, the association between NAFLD and risk of AF was still higher than that in non-NAFLD (RR 1.19, 95% CI 1.04-1.31, I2 = 54.0%). There was significant heterogeneity for the risk of AF between minimally and maximally adjusted models (I2 = 77.1%, P for heterogeneity = 0.04). Compared with non-NAFLD, the absolute risk increase in NAFLD for AF was 1.3 (95% CI 0.5-2.1) per 1000 person-years. Conclusions NAFLD is associated with increased risk of incident AF. The strength of the association between NAFLD and AF is partially attributed to the co-existing cardiometabolic risk factors.

Human perception sensitivity to video distortion is vital for visual quality assessment (VQA). Different from the perception mechanism of image distortion that has been thoroughly studied, the perception of video distortion is inevitably influenced by motion of dynamic content due to the characteristics of the human visual system (HVS). In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to study the human perception sensitivity to quantification-aroused video distortion under various content motion conditions. For this purpose, we conduct experiments to record the EEG signals of the subjects when they are watching distorted videos. According to the feature analysis of EEG data, the P300 component aroused by human perception of video quality change is selected as the indicator of human perception of distortion. By the means of classification based on linear discriminant analysis (LDA), it is found that the separability of the P300 component, which is measured by the area under curve (AUC) of the receiver operating characteristic (ROC), is positively correlated with the perceptibility of distortion. The correlation provides a valid psychophysiological method, which is exempt from being influenced by subjective bias due to human high-level cognitive activities, for evaluating distortion perceptibility. In addition, the regression analysis results demonstrate a sigmoid-typed quantitative relation between the perceptibility of distortion and separability of the P300 component. Based on such relation, the perceptibility thresholds of distortion corresponding to various content motion speeds are calibrated by EEG signals and it is found that the content motion speed has a significant impact on distortion perceptibility.

With the advent of 5G wireless communication systems, multimedia data is predicted to grow rapidly in the near future. As a result, the large amount of multimedia data puts huge pressure on wireless communication, which poses a huge challenge to network capacity. Multimedia data (image/ video) intelligent compression is an effective way to increase the network capacity and improve the user's QoE. The success of image/video compression generally depends on data representations. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation learning algorithms implementing such priors. This article proposes an intelligent computing communication framework to reduce the amount of transferred data. Simultaneously, it reviews the area of representation learning, covering advances in dictionary learning, RoI, and deep learning image/video compression methods. Furthermore, we compare various compression methods as well as point out promising future works.

Despite significant advances in therapies in past decades, the mortality rate of septic cardiomyopathy remains high. The aim of this study is to explore the therapeutic effects of combined treatment using melatonin and irisin in a mouse model of lipopolysaccharide (LPS)-mediated septic cardiomyopathy. Our data found that melatonin and irisin could further attenuate LPS-induced myocardial depression. Molecular investigation illustrated that melatonin and irisin cotreatment sustained cardiomyocyte viability and improved mitochondrial function under LPS stress. Pathway analysis demonstrated that macrophage-stimulating 1 (Mst1), which was significantly activated by LPS, was drastically inhibited by melatonin/irisin cotreatment. Mechanically, Mst1 activated c-Jun N-terminal kinase (JNK) pathway and the latter induced oxidative stress, adenosine triphosphate metabolism disorder, mitochondrial membrane potential reduction, and cardiomyocyte death activation. Melatonin and irisin cotreatment effectively inhibited the Mst1-JNK pathway and, thus, promoted cardiomyocyte survival and mitochondrial homeostasis. Interestingly, Mst1 overexpression abolished the beneficial effects of melatonin and irisin in vivo and in vitro. Altogether, our results confirmed that melatonin and irisin combination treatment could protect heart against sepsis-induced myocardial depression via modulating the Mst1-JNK pathways.

The 360-degree video/image, also called an omnidirectional video/image or panoramic video/image, is very important in some emerging areas such as virtual reality (VR). Therefore, corresponding image generation algorithms are urgently needed. However, existing image generation models mainly focus on 2-D images and do not consider the spherical structures of panoramic images. In this article, we propose a panoramic image generation method based on spherical convolution and generative adversarial networks, called spherical generative adversarial networks (SGANs). We adopt the sketch map as the input, which is a concise geometric structure representation of the panoramic image, e.g., comprising approximately 7% of the pixels for a 583 × 1163 image. Through adversarial learning, a realistic-looking, plausible and high-fidelity spherical image can be obtained from the sparse sketch map. In particular, we build a dataset of the sketch maps using a visual computation-based sketching model. Then, by optimizing SGANs with GAN loss, feature matching loss and perceptual loss, realistic textures and details are recovered gradually. On one hand, it is an improvement using the sparse sketch map as input rather than the denser input, e.g., the features of the textures and colors. On the other hand, spherical convolution helps to remedy space-varying distortions of the planar projection. We conduct extensive experiments on some public panoramic image datasets and compare them with state-of-the-art techniques to validate the superior performance of the proposed approach.

Brain-inspired cognitive systems (BCSs) are an emerging field of cybernetics, cognitive science, and system science. BCSs study not only the intelligence science foundations of artificial intelligence (AI) and cognitive systems, but also formal models of the brain embodied by computational intelligence. This article presents the brain and intelligence science foundations of BCS toward hybrid intelligent systems and the symbiotic intelligence of humanity. It explores the transdisciplinary theoretical foundations of system, brain, intelligence, knowledge, cybernetic, and cognitive sciences toward the next generation of knowledge processors beyond classic data processors for autonomous computing systems. A BCS provides an overarching platform for cognitive cybernetics, humanity, and systems to enable emerging hybrid societies shared by humans and intelligent machines.

To meet the ever-increasing demands for mobile video streaming, proactive caching over the network edge has been proposed as a promising solution for next generation wireless networks. In this paper, we consider the trace-driven cache-enabled video streaming design in the scenario of a metropolis to boost the spectral efficiency on the system side and the quality of experience (QoE) on the user side. A novel scheme to jointly provide proactive caching, power allocation, user association and adaptive video streaming is designed via the formation of a QoE-aware throughput maximization problem. Specifically, the caches are refreshed in the content placement phase according to the resource status and expected traffic, which is obtained by exploring the traces collected over a big city. In addition, users need to be associated with a proper small base station (SBS) in the content delivering phase to provide the highest attainable rate. We demonstrate the effectiveness of the proposed scheme via experiments conducted over real user trace datasets.

Public health decision-making may have great uncertainty especially in dealing with emerging infectious diseases, so it is necessary to establish a collaborative mechanism among modelers, epidemiologists, and public health decision-makers to reduce the uncertainty as much as possible. We searched the relevant studies on transmission dynamics modeling of infectious diseases, SARS, MERS, and COVID-19 as of March 1, 2021 based on PubMed. We compared the key health decision-making time points of SARS, MERS, and COVID-19 prevention and control, and the publication time points of modeling research, to reveal the collaboration between infectious disease modeling and public health decision-making in the context of the COVID-19 pandemic. Searching with infectious disease and mathematical model as keywords, there were 166, 81 and 1 289 studies on the modeling of infectious disease transmission dynamics of SARS, MERS, and COVID-19 were retrieved respectively. Based on the modeling application framework of public health practice proposed in the current study, the collaboration among modelers, epidemiologists and public health decision-makers should be strengthened in the future.

In order to solve the problems of low guidance accuracy and non-convergence of normal overload at the end when the classical proportional guidance method is used in the final guidance phase of UAV in the process of intercepting maneuvering targets, a combined extended proportional guidance method is proposed in combination with the idea of predicted interception point. Using MATLAB simulation tools, the two-dimensional vertical plane of unmanned aerial vehicle intercept the target model respectively by classical proportional guidance method and the combined extended proportional guidance law has carried on the comparative analysis of the simulation results show that this kind of combined extended proportional guidance law not only effectively reduce the terminal error and the end of the normal overload level, but also can effectively track maneuvering target circular arc.

Viral myocarditis has been found to be one of the leading causes of sudden death in young adults. However, no effective drugs have been developed to intervene the progression of myocarditis. Accordingly, the present study is carried out to explore the protective role played by melatonin in the setting of viral myocarditis with a focus on Mst1-Hippo pathway, mitochondrial dysfunction and ER stress. Cardiac function was determined via echocardiographic examination. Mitochondrial function and ER stress were detected via ELISA, western blots, and immunofluorescence. Our data demonstrated that virus injection induced cardiac dysfunction as evidenced by reduced contractile function in myocardium. Besides, LDH release assay and western blotting analysis demonstrated that cardiomyocyte death was activated by virus injection. Interestingly, melatonin treatment improved cardiac function and repressed virus-mediated cardiomyocyte apoptosis. At the molecular levels, mitochondrial dysfunction was induced by virus infection, as indicated by mitochondrial membrane potential reduction, mPTP opening rate elevation and caspase-9-related apoptosis activation. Besides, ER stress parameters were also elevated in virus-treated cardiomyocytes. Interestingly, melatonin treatment maintained mitochondrial dysfunction and repressed ER stress. To the end, we found that Mst1 was upregulated by virus infection; this effect was attenuated through supplementation with melatonin. However, Mst1 overexpression reduced the beneficial impact exerted by melatonin on cardiomyocyte viability, mitochondrial function and ER homeostasis. Our study illustrated that melatonin treatment attenuated viral myocarditis via sustaining cardiomyocyte viability, repressing mitochondrial dysfunction and inhibiting ER stress in a manner dependent on Mst1 inhibition.

Quality of experience (QoE) serves as a direct evaluation of users’ experiences in mobile video transmission and thus essential for network management, such as network optimization. In this paper, we propose a deep learning-based QoE prediction approach with a large-scale QoE dataset for mobile video transmission. Specifically, we develop a mobile phone application for collecting user QoE data when viewing videos transmitted over the mobile internet in a practical environment. Then, we construct a large-scale dataset by collecting over 80000 piece of data with four kinds of subjective scores and 89 network parameters. Each QoE metric is related to only some of the 89 network parameters. Therefore, we apply the feature selection method to find the feature parameters related to user scores. Additionally, the boxplot method is used to clean the raw data by removing outliers. Finally, a deep neural network (DNN) is developed to learn the relationships between the network parameters and the subjective QoE scores. The proposed DNN can also be seen as a data-driven objective QoE prediction approach for mobile video transmission, which can be used to predict the user QoE scores. The experimental results show that the proposed approach can effectively remove most features irrelevant to QoE prediction. Moreover, the performance of QoE prediction by the proposed model outperforms other state-of-the-art approaches.

Satellite networks are able to provide seamless services for ground users with wide coverage, which is intrinsically suitable for providing broadcasting or multicasting services. While terrestrial networks have experienced rapid development in recent years, in next generation communications, the architecture of integrated terrestrial-satellite networks is promising to enable users to seamlessly access all types of services. In this article, we investigate the problem of cooperative transmission in integrated terrestrial-satellite networks, where two cases of unicast transmission and multicast transmission are discussed separately. Overall, this article aims to provide a comprehensive discussion and also propose a general framework of cooperative transmission in future terrestrial- satellite networks.

With the proliferation of mobile video services in next-generation wireless networks, network optimization is progressing in the direction of improving QoE. This phenomenon is due to the fact that traditional QoS-oriented network optimization for video services fails to effectively configure network resources and enhance user satisfaction. Recently, QoE-driven resource allocation methods in different scenarios have been corroborated to outperform traditional QoS-oriented methods. It has been realized that reliable QoE models and effective optimization schemes are crucial to these methods. In this article, we consider a learning based QoE model from real-world data and apply it to network resource optimization. It is worth mentioning that human perception limits of video viewing are studied in the process of QoE modeling. Based on this model, the upper-layer optimization method suitable for the general wireless communication scenario and the lower-layer optimization method with the caching framework as the kernel are investigated respectively. The goal of this article is to provide an overview of the QoE-driven resource allocation architecture and to have a glimpse of the methodology employed in QoE modeling and network optimization.

3-D reconstruction is at the core for many driving applications of Internet of Intelligent Vehicles. Previous works on reconstruction of a 3-D point shape commonly use a two-step framework. Precisely localizing a series of feature points in an image is performed on the first step. Then the second procedure attempts to fit the 3-D data to the observations to get the real 3-D shape. Such an approach has high time consumption, and easily gets stuck into local minimum. To address this problem, we propose a method to jointly estimate the global 3-D geometric structure of car and localize 2-D landmarks from a single viewpoint image. First, we represent the 3-D shape with a set of predefined shape bases, while parametrizing it by the coefficients of the linear combination of them. Second, we adopt a cascaded regression framework to regress the global shape encoded by the prior bases, by jointly minimizing the appearance and shape fitting differences. The position fitting item can help cope with the description ambiguity of local appearance, and provide more information for 3-D reconstruction. We apply the proposed approach on a multiview car dataset. Experimental results demonstrate favorable improvements on pose estimation and shape prediction, compared with some previous methods.

Recent studies have greatly promoted the development of semantic segmentation. Most state-of-the-art methods adopt fully convolutional networks (FCNs) to accomplish this task, in which the fully connected layer is replaced with the convolution layer for dense prediction. However, standard convolution has limited ability in maintaining continuity between predicted labels as well as forcing local smooth. In this paper, we propose the dense convolution unit (DCU), which is more suitable for pixel-wise classification. The DCU adopts dense prediction instead of the center-prediction manner used in current convolution layers. The semantic label for every pixel is inferred from those overlapped center/off-center predictions from the perspective of probability. It helps to aggregate contexts and embeds connections between predictions, thus successfully generating accurate segmentation maps. DCU serves as the classification layer and is a better option than standard convolution in FCNs. This technique is applicable and beneficial to FCN-based state-of-the-art methods and works well in generating segmentation results. Ablation experiments on benchmark datasets validate the effectiveness and generalization ability of the proposed approach in semantic segmentation tasks.

With the popularity of handheld devices, the development of wireless communication technology and the proliferation of multimedia resources, mobile video has become the main business in LTE networks with explosive traffic demands. How to improve the quality of experience (QoE) of mobile video in the dynamic and complex network environment has become a research focus. Dynamic adaptive streaming over HTTP technology introduces adaptive bitrate (ABR) requests at the client side to improve video QoE and various rate adaptation algorithms are also constantly proposed. In view of the limitations of the existing heuristic or learning-based ABR methods, we propose redirecting enhanced Deep Q-learning toward DASH video QoE (RDQ), a QoE-oriented rate adaptation framework based on enhanced deep Q-learning. First, we establish a chunkwise subjective QoE model and utilize it as the reward function in reinforcement learning so that the strategy can converge toward the direction of maximizing the subjective QoE score. Then, we apply several effective improvements of deep Q-learning to the RDQ agent’s neural network architecture and learning mechanism to achieve faster convergence and higher average reward than other learning-based methods. The proposed RDQ agent has been thoroughly evaluated using trace-based simulation on the real-time LTE network data. For disparate network scenarios and different video contents, the RDQ agent can outperform the existing methods in terms of the QoE score. The breakdown analysis shows that RDQ can suppress the number and the duration of the stalling events to the minimum while maintaining high video bitrate, thus achieving better QoE performance than other methods.

Recent region proposal generation methods show a low Intersection-of-Union with the ground-truth boxes. Because they simply regress the coordinates of the bounding boxes by exploiting the single-layer output of convolutional neural networks. This paper proposes a hierarchical objectness network for region proposal generation and object detection to address the inaccurate localization problem. Instead of regressing the coordinates, we subtly localize the objects by predicting the stripe objectness, i.e., a group of probabilities reflecting the existence of the object in each location of the candidate proposal. Additionally, we construct the hierarchical features by reversely connecting multiple convolutional layers to detect objects with large-scale variations. Our experimental results demonstrate that our method performs better than the state-of-the-art region proposal generation methods in terms of recall. Moreover, by integrating with advanced object detection frameworks, our method achieves superior object detection results.

The existing image compression methods usually choose or optimize low-level representation manually. Actually, these methods struggle for the texture restoration at low bit rates. Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. To achieve better perceptual quality, generative models are widely used, especially generative adversarial networks (GAN). However, training GAN is intractable, especially for high-resolution images, with the challenges of unconvincing reconstructions and unstable training. To overcome these problems, we propose a novel DNN-based image compression framework in this paper. The key point is decomposing an image into multi-scale sub-images using the proposed Laplacian pyramid based multi-scale networks. For each pyramid scale, we train a specific DNN to exploit the compressive representation. Meanwhile, each scale is optimized with different aspects, including pixel, semantics, distribution and entropy, for a good “rate-distortion-perception” trade-off. By independently optimizing each pyramid scale, we make each stage manageable and make each sub-image plausible. Experimental results demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in providing improved visual quality. Additionally, a better performance in the down-stream visual analysis tasks which are conducted on the reconstructed images, validates the excellent semantics-preserving ability of the proposed method.

In view of the high mortality rate and complications of acquired pneumonia (AP), the improved autoregressive integrated moving average (ARIMA) model was applied to predict the early prognostic factors of patients with AP. First, a multi-factor mixed forecast ARIMA-classification and regression trees (CART) classification tree model was established in this study, and the ARIMA-CART model was used for time series fitting analysis to observe the incidence trend of AP. Finally, a predictive model for the risk and prognosis of elderly patients with AP was constructed. The experimental results proved that the serum creatine kinase index and lactate dehydrogenase index of patients with AP had a resistance effect on their prognosis. Therefore, the serum creatine kinase index and lactate dehydrogenase index of patients with AP should be dealt with in actual medical scenarios. The indexes can be reasonably detected and can effectively improve the prognosis of patients. This had a certain reference for the promotion of ARIMA model in the research of early prognostic factors in patients with AP.

This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how do the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-Label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems., NAACL 2021 Industry track

With the development of mobile Internet technologies, wireless communication is facing huge challenges under the explosive growth of multimedia data, e.g. video conferences, online education. This makes it difficult to guarantee the communication quality where communication resources (bandwidth, channel, etc.) are limited. In this paper, we propose an image enhancement method to transform blurred images into images with high perceptual quality. The proposed method serves as a post-processing part for communication systems and is incorporated into the receiver. Specifically, we learn the prior of high quality images using a collected dataset. We train a neural network to accomplish this task and adopt a multi-scale perceptual loss as the objective, which is more consistent with the quality of experience (QoE). To validate the proposed method, we train our model on a large dataset with both blurred images and high quality images. Experimental results show that, using a pre-collected dataset with high quality images, the proposed approach can effectively restore the blurred images.

Background: At present, the epidemic of the novel coronavirus disease 2019 (COVID-19) has quickly engulfed the world. Inflammatory cytokines are associated with the severity and outcomes of patients with COVID-19. However, the prognostic value of pro-inflammatory factors in cancer patients with COVID-19 are unknown. Methods: A multi-center, retrospective, cross-sectional study, based on five designated tertiary hospitals for the treatment of COVID-19 in Hubei Province, China. 112 cancer patients with COVID-19, and 105 COVID-19 patients without cancer were enrolled in the study between January 1st, 2020 and April 30th, 2020. The risk assessment of pro-inflammatory factors for disease severity and clinical adverse outcomes was identified by univariable and multivariable logistic regression models. Results: Of the 112 cancer patients with COVID-19, 40 (35.7%) patients were in critical condition and 18 (16.1%) patients died unfortunately. Univariate and multivariate analysis demonstrated that hemoglobin level and pro-inflammatory neutrophils and C-reactive protein (CRP), can be used as independent factors affecting the severity of COVID-19; Meanwhile, pro-inflammatory neutrophils and CRP can be used as an independent influencing factor for adverse clinical outcome of death. Moreover, the dynamic changes of neutrophils and CRP were also presented, and compared with COVID-19 patients without cancer, cancer patients with COVID-19 showed higher neutrophil counts and CRP levels. Conclusion: In cancer patients with COVID-19, the significant increase in pro-inflammatory neutrophils and CRP indicated a more critical illness and adverse clinical outcome, and pro-inflammatory neutrophils and CRP played a greater adverse role compare with COVID-19 patients without cancer, which may be the cause of critical illness and adverse clinical outcomes of cancer patients with COVID-19.

Recently, online office work and education through video conferences have become a typical part of people’s daily life, increasing the need for bandwidth and storage of wireless communication networks. To avoid video stalling and blurring, this paper proposes a content-aware facial image compression method based on deep learning networks at low bit rates for higher quality of experience (QoE). In this paper, the average face calculated from the database collected by the server is incorporated into the face representation model, which is used as prior knowledge to improve the performance of the video communication system. With the face representation model, facial images are decomposed into shape components and texture components, and different compression methods are applied to these components according to different redundancy levels. Specifically, a convolutional neural network (CNN) method is proposed to process texture components. The texture components are optimized by end-to-end rate-distortion learning with mean squared error (MSE) and structural similarity (SSIM) as optimization objectives for high fidelity reconstruction. The shape components use quantizer and entropy coding to preserve the details. Compared with traditional compression methods JPEG and JPEG2000, our proposed method could achieve a better performance in terms of PSNR and SSIM values at low bit rates.

Time-sensitive services (TSSs) have been widely envisioned for future sixth generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the quality of service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyzing the random delay characteristics. In this paper, an MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay bound violation probability, the average delay and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate elaborate allocation of communication and computation resources to improve the QoS of TSSs., 17 pages, 11 figures. This paper has been accepted by IEEE Internet of Things Journal

Quantum secure direct communication (QSDC) is capable of direct confidential communications over a quantum channel, which is achieved by dispensing with the key agreement channel of the well-known quantum key distribution (QKD). However, to make QSDC a practical reality, we have to mitigate its reliance on quantum memory, its immediate communication interruption caused by eavesdropping and its low transmission reliability due to the heavy qubit losses. Hence a new QSDC protocol is proposed based on a sophisticated coded single-photon DL04 QSDC protocol to tackle the open challenges. In particular, quantum memory is dispensed with and a high-accuracy secrecy capacity estimate is derived for this protocol by conceiving dynamic joint encryption and error-control (JEEC) coding. We demonstrate that this quantum-memory-free DL04 QSDC (QMF-DL04 QSDC) protocol inches closer to the quantum channel's capacity and significantly improves the original DL04 QSDC's robustness. Moreover, a rate-compatible low-rate JEEC coding scheme is designed for the proposed framework, and the JEEC code advocated is shown to approach the secrecy capacity, despite tolerating an extremely high loss of qubits in the time-varying wiretap channel. Our simulations and experimental results demonstrate that the QMF-DL04 QSDC scheme significantly increases both the secure information rate and the communication distance of the original DL04 protocol.

Study Design: A retrospective cohort study. Objective: To evaluate the effect of O-arm navigation in percutaneous surgeries for thoracolumbar fracture in comparison to the use of conventional fluoroscopic technique. Summary of Background Data: O-arm navigation is a progressive surgical tool, with extensive research papers reporting its effects. Whereas, there were not many papers describing its accuracy and facet impingement rate when compared with fluoroscopy-guided technique in percutaneous surgeries, especially at varying fracture levels. Materials and Methods: We conducted a retrospective comparative study of 97 consecutive patients of single-level neurological intact thoracolumbar fractures from November 2015 to October 2017 and they were all treated with percutaneous pedicle screw implantation. Screws were classified as 4 grades of perforations and 3 grades of facet joint violation. The association between variables such as anatomic perforation, functional perforation, and facet impingement were investigated by χ2 test, Fisher exact test or t test. A P-value of

Image captioning is a challenging problem owing to the complexity of image content and the diverse ways of describing the content in natural language. Although current methods have made substantial progress in terms of objective metrics (such as BLEU, METEOR, ROUGE-L and CIDEr), there still exist some problems. Specifically, most of these methods are trained to maximize the log-likelihood or objective metrics. As a result, these methods often generate rigid and semantically incomplete captions. In this paper, we develop a new model that aims to generate captions conforming to human evaluation. The core idea is to use local-to-global semantic supervised learning by introducing the two-level optimization objective functions. At the word level, we match each word to the image regions using the local attention objective function; at the sentence level, we align the entire sentence and the image using the global semantic objective function. Experimentally, we compare the proposed model with current methods on MSCOCO dataset. We show that either local attention supervision or global semantic supervision is the necessary component for the success of our model through ablation studies. Furthermore, combining these two supervision objective functions achieves state-of-the-art performance in terms of both standard evaluation metrics and human judgment.

BackgroundSince December 2019, SARS-CoV-2 has extended to most parts of China with >80 000 cases and to at least 100 countries with >60 000 international cases as of 15 March 2020. Here we used a household cohort study to determine the features of household transmission of COVID-19.MethodsA total of 105 index patients and 392 household contacts were enrolled. Both index patients and household members were tested by SARS-CoV-2 RT-PCR. Information on all recruited individuals was extracted from medical records and confirmed or supplemented by telephone interviews. The baseline characteristics of index cases and contact patients were described. Secondary attack rates of SARS-CoV-2 to contact members were computed and the risk factors for transmission within the household were estimated.ResultsSecondary transmission of SARS-CoV-2 developed in 64 of 392 household contacts (16.3%). The secondary attack rate to children was 4% compared with 17.1% for adults. The secondary attack rate to the contacts within the households with index patients quarantined by themselves since onset of symptoms was 0% compared with 16.9% for contacts without quarantined index patients. The secondary attack rate to contacts who were spouses of index cases was 27.8% compared with 17.3% for other adult members in the households.ConclusionsThe secondary attack rate of SARS-CoV-2 in household is 16.3%. Age of household contacts and spousal relationship to the index case are risk factors for transmission of SARS-CoV-2 within a household. Quarantine of index patients at home since onset of symptoms is useful to prevent the transmission of SARS-Co-2 within a household.

Objective Gut microbiota-produced trimethylamine N-oxide (TMAO) is a risk factor for cardiovascular events. However, conflicting findings regarding the link between plasma TMAO level and prognosis for patients with heart failure have been reported. We examined the association of plasma TMAO concentration with risk of major adverse cardiac events (MACEs) and all-cause mortality in patients with heart failure. Study design Meta-analysis of prospective clinical studies. Data sources We searched electronic databases (PubMed, EMBASE) for published prospective studies examining associations between plasma TMAO level and MACEs and all-cause mortality in adults with heart failure. Data synthesis Hazard ratios (HRs) with 95% confidence intervals for associations between TMAO level and outcomes were estimated in random effects models. In seven eligible studies including a total of 6879 patients (median follow-up, 5.0 years) and adjusted for multiple risk factors, higher plasma TMAO level was associated with greater risks of MACEs (TMAO tertile 3 v tertile 1: HR, 1.68; 95% CI, 1.44-1.96; per SD increment: HR, 1.26; 95% CI, 1.18-1.36) and of all-cause mortality (TMAO tertile 3 v tertile 1: HR, 1.67; 95% CI, 1.17-2.38; per SD increment: HR, 1.26; 95% CI, 1.07-1.48). Higher TMAO level was also associated with greater risk of MACEs after adjusting for estimated glomerular filtration rate (eGFR; six studies included); however, the heterogeneity of studies in which risk was adjusted for eGFR was significant (I2 = 76%). Conclusions Elevated plasma TMAO level in patients with heart failure is associated with poorer prognoses. This association is only partially mediated by renal dysfunction.

Current protocol of Amide Proton Transfer-weighted (APTw) imaging commonly starts with the acquisition of high-resolution T2-weighted (T(2)w) images followed by APTw imaging at particular geometry and locations (i.e. slice) determined by the acquired T(2)w images. Although many advanced MRI reconstruction methods have been proposed to accelerate MRI, existing methods for APTw MRI lacks the capability of taking advantage of structural information in the acquired T(2)w images for reconstruction. In this paper, we present a novel APTw image reconstruction framework that can accelerate APTw imaging by reconstructing APTw images directly from highly undersampled k-space data and corresponding T(2)w image at the same location. The proposed framework starts with a novel sparse representation-based slice matching algorithm that aims to find the matched T(2)w slice given only the undersampled APTw image. A Recurrent Feature Sharing Reconstruction network (RFS-Rec) is designed to utilize intermediate features extracted from the matched T(2)w image by a Convolutional Recurrent Neural Network (CRNN), so that the missing structural information can be incorporated into the undersampled APT raw image thus effectively improving the image quality of the reconstructed APTw image. We evaluate the proposed method on two real datasets consisting of brain data from rats and humans. Extensive experiments demonstrate that the proposed RFS-Rec approach can outperform the state-of-the-art methods.

Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-level robustness by adding input noise, which is beneficial for text classification. However, it lacks sufficient contextual information enhancement and thus is less useful for sequence labelling tasks such as chunking and named entity recognition (NER). To address this limitation, we propose masked adversarial training (MAT) to improve robustness from contextual information in sequence labelling. MAT masks or replaces some words in the sentence when computing adversarial loss from perturbed inputs and consequently enhances model robustness using more context-level information. In our experiments, our method shows significant improvements on accuracy and robustness of sequence labelling. By further incorporating with ELMo embeddings, our model achieves better or comparable results to state-of-the-art on CoNLL 2000 and 2003 benchmarks using much less parameters.

Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.

Random access is a key technology in satellite communication, as a large number of machine- type communication (MTC) terminals accessing the satellite makes it difficult to guarantee the access quality. Irregular repetition slotted ALOHA (IRSA) is one random access protocol relying on transmitting irregular number of replicas in multiple time slots, achieving a peak throughput at 0.8 in practical implementations. However, the probability of sending a certain number of replicas stays the same when given degree distribution, without considering the effects of different loads, which means there are extra useless packets sent and brings power waste in IRSA. Therefore, enhanced irregular repetition slotted ALOHA (EIRSA) based on tracking degree distribution control (TDDC) algorithm is proposed in this paper with adaptive degree distribution adjustment scheme to reduce the number of replicas while maintaining the same access performance with adaptation. Simulation results show that proposed protocol can achieve higher performance at the same power level and it is adaptive to load change.

Currently, multimedia data grow in a explosive manner, whereas the effective bandwidth is quite limited in the wireless communications, such as high definition image/video communication, satellite communication and so on. Generative adversarial network (GAN) based image compression goes beyond the traditional image encoding standards under a obviously low bitrate. However, the most existing methods do not consider the importance of different image contents, where the regions with complex and saliency structures generally are more essential to users' quality of experience (QoE). In this paper, we propose a content-aware deep perceptual image compression method (CDPIC). The proposed approach involves a saliency subnet to produce content saliency map for adaptive bit allocation. Moreover, using QoE as the ultimate goal, our approach designs different compression objectives for different image contents, where it tends to preserve more details for the regions with complex and saliency structures, and improves the perceptual quality of the background region. Our experiments show that the proposed approach outperforms the state-of-the-art methods, and produces advanced performance in preserving details and semantics.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max–min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

Estimating 3D shape of an object from 2D observations in a monocular image is fundamentally an inverse problem due to the ambiguity of the projection from 3D to 2D and becomes more challenging when there are undesirable outliers in the observations. In this paper, we develop a robust model to estimate 3D shape from 2D landmarks with an unknown camera pose. The 3D shape of the object is assumed as a linear combination of a group of prior shape bases. At the same time, we explicitly model the outliers as sparse noises to handle severely contaminated observations. The objective function is nonconvex and nonsmooth constrained on Stiefel manifold, where the coupling of underdetermined shape representation coefficients and camera pose makes it more difficult to solve. We first propose a numerical algorithm based on alternating direction method of multipliers for the no-outlier case. We set the orthogonality constraints into the smooth subproblem, which admits a closed-form solution, and the other subproblems are all well known and can be easily solved. We then extend this algorithm to the proposed robust model. The proposed algorithms can achieve convergence rapidly. The experimental results on both synthetic data and real data show that the proposed method outperforms the other methods.

Summary In this paper, an improved immersed boundary-lattice Boltzmann method based on the force correction technique is presented for fluid-structure interaction problems including the moving boundary interfaces. By introducing a force correction coefficient, the non-slip boundary conditions are much better enforced compared with the conventional immersed boundary-lattice Boltzmann methods. In addition, the implicit and iterative calculations are avoided; thus, the computational cost is reduced dramatically. Several numerical experiments are carried out to test the efficiency of the method. It is found that the method has the second-order accuracy, and the non-slip boundary conditions are enforced indeed. The numerical results also show that the present method is a suitable tool for fluid-structure interaction problems involving complex moving boundaries.

This paper attempts to fuse a multispectral image and an auxiliary hyperspectral image (HSI) with no requirement of image registration. Most previous studies solve this problem with sparsity-based methods. However, in this paper, a novel fusion framework is developed based on a Gaussian mixture model (GMM): First, the GMM is adopted to extract the spectral information from the input HSI. Low-rank constraints are imposed on the covariance matrices of the model to solve the computational problem in the expectation-maximization approach. Second, considering the spatial self-similarity, a structure-similarity regularization term is designed to further enhance the quality of the reconstructed image. To that end, a forward–backward splitting method is adopted to cut down the computational complexity of the optimization. The proposed method does not require two well-aligned images, thus, it will not be influenced by the registration errors between two fusing images. Experimental results of a simulated data set and an actual satellite (EO-1/Hyperion/ALI) data set show that the proposed method displays a stable performance and outperforms many state-of-the-art methods with acceptable computational complexity, when registration errors are taken into consideration.

This paper presents a hierarchical multinomial latent model with G0 distribution for synthetic aperture radar (SAR) image semantic segmentation. The model considers the scattering statistics and multiscale characteristics of the SAR images. Specifically, a 2-D discrete wavelet transform is used to construct a quad-tree structure, and a hierarchical multinomial latent model is used for semantic segmentation. The hierarchical model can capture local and global characteristics of the SAR images. Additionally, statistical distributions of SAR images are discussed. The flexible G0 distribution is substituted into the hierarchical multinomial latent model for the segmentation of various types of land covers. Moreover, the developed Bayesian inference on the quad-tree structure is incorporated into our approach. The semantic segmentation map is obtained from an initialization, bottom-up, and top-down probability computations. In this way, the underlying spatial structure information is effectively propagated. Our experiments compare the proposed approach, other multinomial latent model-based methods, and Markov random field-based methods. The experiments are conducted from a series of synthetic and real SAR images, where the segmentation results demonstrate that our approach is robust to the noise in most cases, obtains the best result among the compared methods and improves the state-of-the-art segmentation performance.

Background Essential metals play critical roles in fetal growth and development, but results from human studies are inconsistent. Additionally, whether maternal thyroid hormone (TH) levels mediate the associations between essential metals and fetal growth remains unknown. Methods Data for analysis were extracted from the Information System of Guangdong Women and Children Hospital between January 2017 and December 2019. Maternal levels of essential metals [copper (Cu), zinc (Zn), magnesium (Mg), and iron (Fe)] and THs were measured at the second trimester. Multivariate linear models were introduced to evaluate the potential associations between maternal essential metals, thyroid functions, and fetal growth, and the possible mediation effects of thyroid functions were explored in the median analyses. Results A total of 4186 mother-infant pairs were included in the present study. Maternal Fe levels were found to significantly increase birth weight in 272.91 g (95 % CI: 15.59, 530.22) among anemia group. Maternal Cu levels were positively associated with increased free triiodothyronine/free thyroxine ratio (FT3/FT4). Negative associations of Fe and Mg levels with thyroid-stimulating hormone (TSH) concentrations were observed, accompanied with the positive associations in relation to FT3, FT4 and FT3/FT4 ratio. Mediation analyses suggested that 72.01 % of the associations between Fe levels and birth length might be mediated by FT3 levels. Additionally, 25.85 % of the Cu-birth length association and 44.53 % of the Fe-birth length association could be explained by FT3/FT4 ratio. Conclusion Our findings suggest that maternal Cu, Mg, and Fe levels can alter TH concentrations, and maternal FT3 and FT3/FT4 ratio might be potential mediators on the developmental effects of Cu and Fe levels.

This paper focuses on fusing hyperspectral and multispectral images with an unknown arbitrary point spread function (PSF). Instead of obtaining the fused image based on the estimation of the PSF, a novel model is proposed without intervention of the PSF under Bayesian framework, in which the fused image is decomposed into double subspace-constrained matrix-factorization-based components and residuals. On the basis of the model, the fusion problem is cast as a minimum mean square error estimator of three factor matrices. Then, to approximate the posterior distribution of the unknowns efficiently, an estimation approach is developed based on variational Bayesian inference. Different from most previous works, the PSF is not required in the proposed model and is not pre-assumed to be spatially invariant. Hence, the proposed approach is not related to the estimation errors of the PSF and has potential computational benefits when extended to spatially variant imaging system. Moreover, model parameters in our approach are less dependent on the input data sets and most of them can be learned automatically without manual intervention. Exhaustive experiments on three data sets verify that our approach shows excellent performance and more robustness to the noise with acceptable computational complexity, compared with other state-of-the-art methods.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max–min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods.

To cope with increasing vehicular traffic and extensive application demands on vehicular cellular networks, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication contacts when vehicles come into the communication range of each other, which offloads significant traffic from the cellular network. The current opportunistic vehicular data transmission models often do not take into account the fact that the contact duration is usually very short, due to the high mobility of vehicles, which will limit the amount of data that can be transmitted during one opportunistic contact. In this paper, we consider a mobile data offloading system that integrates cellular network and vehicular opportunistic communications. Based on this proposed system, we establish a mathematical framework to study the problem of contact-aware optimal resource allocation for mobile data offloading by the explicit consideration of the contact duration. Based on theoretical analysis of this problem, we derive an optimal scheme for mobile data offloading to optimally allocate the network resources in terms of opportunistic contacts and offloading helpers’ storage. By carrying out extensive simulation using two realistic urban vehicular traces, we demonstrate the effectiveness of our contact duration aware optimal offloading scheme, in comparison with a wide range of existing schemes.

Modern steel rolling process is commonly designed to produce products with a variety of grade specifications. There exist enormous historical data for the typical products in large batch production; while for small-batch customized products, the lack of sufficient historical data may prevent successful application of traditional data-based process modeling and quality prediction methods. In this paper, a practical and effective strategy is developed for fast modeling and flatness defect prediction of a steel rolling process. The key idea is based on model migration, assuming that high-performance quality prediction models (defined as Base Models) have been available for the typical products. A Principal Component Analysis (PCA) similarity indicator is adopted to measure the difference between new operating modes and the typical operating modes, based on which, new operating modes are divided into non-significantly-changed modes and significantly-changed modes. For the former, the new flatness defect prediction model is developed by screening the similar data in the typical modes and augmenting them into the modeling data set for the new mode. For the latter, the new model is obtained by reconstructing the base model via parameters’ shifting and scaling. Case study results can show the validity of the proposed method.