In conclusion, we have proposed a six-deep-feature radiomics signature that have the potential to be an imag- ing biomarker for prediction of the OS in patients with GBM. It was demonstrated that the deep learning method can be incorporated into the state-of-the-art radiomics model to achieve a better performance. The proposed signature predicted the OS in GBM patients with better performance compared with conventional factors such as age and KPS. A nomogram was proposed for prediction of the probability of survival. Despite the limitations, the proposed radiomics model has the potential to facilitate the preoperative care of patients with GBM 解释

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In the proposed DLR, a convolutional neural network (CNN) is used. CNN is a representative method used for deep learning, and it has been successfully applied to the field of image segmentation8. Recently, many groups have used CNN for the segmentation of medical images, and it has provided better results than traditional meth- ods9. In glioma segmentation based on MR images, most of the CNN methods were proposed for high-grade gliomas10, 11. Compared with high-grade gliomas, low-grade gliomas are smaller and have lower contrast with the surrounding tissues12. Existing CNN structures would not work well for segmentation of low-grade gliomas. A major architecture adjustment of CNN is therefore essential for both image segmentation and feature extraction. To address the challenging characteristics of low-grade gliomas, we used a modified CNN architecture with 6 convolutional layers and a fully connected layer with 4096 neurons for segmentation. 解释

这段文字讲述了一种名为DLR的分割方法，其中使用了卷积神经网络（CNN），这是一种应用于深度学习的代表性方法，并且已经成功地应用于图像分割领域。最近，许多团队已经将CNN用于医学图像分割，并且相比传统方法，CNN...

检查下列语句的语法和拼写问题。Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

我们可以看到，本文提出的综合预测模型基于时间卷积注意网络（TCAN）和双向门递归单元（BiGRU）网络，并通过奇异谱分析（SSA）和改进的量子粒子群优化算法（IQPSO）进行优化。实验表明，该模型具有更高的预测精度。

In this study we apply methods to determine the tendency for thrombus formation in different central venous catheters (CVC) models associated with flow rate variation. To calculate the thrombogenic potential, we proposed a new numerical model of the platelet lysis index (PLI) equation. To compare the results of PLI and flow rate in different models of catheters, numerical calculations were performed on three different tips of CVC. The results showed that the PLI increases as a power function of the flow rate independent of the type of CVC. This study evidenced that the higher the blood flow rate used in the catheter, the greater the potential for thrombus formation. The PLI computed at the catheter outlet presented higher values when compared to the values computed at the vein outlet indicating that the blood flow through the CVC arterial lumen presents a proportionally larger thrombogenic potential when compared to the blood flow that leaves the vein towards the atrium. This finding may have consequences for clinical practice, since there is no specific flow value recommended in the catheter when the hemodialysis machine is turned on, and with this equation it was possible to demonstrate the thrombogenic potential that the flow rate can possibly offer.

本研究应用方法，确定不同中心静脉导管（CVC）型号在流量变化下形成血栓的倾向。为了计算血栓形成潜力，我们提出了一个新的血小板溶解指数（PLI）方程数值模型。为了比较不同型号导管中PLI和流量的结果，我们在三个...

帮我润色论文。In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that alleviates the influence of samples that cause an overfitted decision boundary. The proposed loss efficiently improves the performance of any type of imbalance learning methods. In experiments on multiple benchmark data sets, we demonstrate the validity of our method and reveal that the proposed loss outperforms the state-of-the-art cost-sensitive loss methods. Furthermore, since our loss is not restricted to a specific task, model, or training method, it can be easily used in combination with other recent resampling, meta-learning, and cost-sensitive learning methods for class-imbalance problems. Our code is made available at https://github.com/pseulki/IB-Loss.

在本文中，我们提出了一种平衡训练方法，以解决不平衡数据学习中的问题。为此，我们在平衡训练阶段推导出一种新的损失，以减少导致过度拟合决策边界的样本的影响。所提出的损失有效提高了任何类型的不平衡学习方法的...

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

这段文字讨论了异质网络方法相对于同质网络方法更接近现实的原因，并介绍了知识图谱和知识图谱嵌入模型在链接预测任务中的应用。作者提出了一些方法来解决多信息融合和归纳能力限制等问题，包括使用药物的局部信息如...

润色下面英文：The controlled drug delivery systems, due to their precise control of drug release in spatiotemporal level triggered by specific stimulating factors and advantages such as higher utilization ratio of drug, less side-effects to normal tissues and so forth, provide a new strategy for the precise treatment of many serious diseases, especially tumors. The materials that constitute the controlled drug delivery systems are called “smart materials” and they can respond to the stimuli of some internal (pH, redox, enzymes, etc.) or external (temperature, electrical/magnetic, ultrasonic and optical, etc.) environments. Before and after the response to the specific stimulus, the composition or conformational of smart materials will be changed, damaging the original balance of the delivery systems and releasing the drug from the delivery systems. Amongst them, the photo-controlled drug delivery systems, which display drug release controlled by light, demonstrated extensive potential applications, and received wide attention from researchers. In recent years, photo-controlled drug delivery systems based on different photo-responsive groups have been designed and developed for precise photo-controlled release of drugs. Herein, in this review, we introduced four photo-responsive groups including photocleavage groups, photoisomerization groups, photo-induced rearrangement groups and photocrosslinking groups, and their different photo-responsive mechanisms. Firstly, the photocleavage groups represented by O-nitrobenzyl are able to absorb the energy of the photons, inducing the cleavage of some specific covalent bonds. Secondly, azobenzenes, as a kind of photoisomerization groups, are able to convert reversibly between the apolar trans form and the polar cis form upon different light irradiation. Thirdly, 2-diazo-1,2-naphthoquinone as the representative of the photo-induced rearrangement groups will absorb specific photon energy, carrying out Wolff rearrangement reaction. Finally, coumarin is a promising category photocrosslinking groups that can undergo [2＋2] cycloaddition reactions under light irradiation. The research progress of photo-controlled drug delivery systems based on different photo-responsive mechanisms were mainly reviewed. Additionally, the existing problems and the future research perspectives of photo-controlled drug delivery systems were proposed.

控制药物释放系统是指可以控制药物空间和时间释放的系统，可以通过一些特定的刺激因素激活，具有药物利用率更高、对正常组织的副作用更小等优势，为精确治疗许多严重疾病，尤其是肿瘤提供了新的策略。...

Motivated by the above discussion, a multi-leader multifollower Stackelberg game architecture is proposed to formulate the interaction between BSs and UEs. In this game, BSs have an advantage as the first mover and can be regarded as the market leaders. UEs are assumed to be in the position of followers in this market. There is a sequential relationship between the actions of the participants. Therefore, the Stackelberg model is more suitable than the Cournot model. Under the proposed architecture, a user association algorithm based on joint UE demand shaping and BS demand response is proposed. With this framework, we can maximize the UE utility function and apply flexible payoff functions to BSs and UEs to design a load-balancing algorithm.翻译

在上述讨论的基础上，提出了一个多领导者多追随者Stackelberg博弈架构，以建立基站和UE之间的相互作用。在这个博弈中，基站作为先行者具有优势，可以被视为市场领导者。UE设备被假设为市场中的追随者。...

In the proposed algorithm, the access prices of BSs are adjusted according to incomes and load situations in the leaderlevel game, and each UE independently chooses the BS that maximizes its payoff (or minimizes its payment) in the follower-level game. Thus, the algorithm achieves optimization in a distributed manner. When a small BS is overloaded, the price will increase to ensure that the offloaded UE has a higher access payment. For the design of the price, a particle swarm optimization-based pricing strategy (PSO-PS) is proposed to maximize the BS revenue. Finally, we use the resident-oriented Gale-Shapley (GS) algorithm to obtain a stable single-BS association. The main contributions of this paper can be summarized as follows:翻译

在所提出的算法中，基站的接入价格根据领导者层次博弈中的收入和负载情况进行调整，而每个UE在追随者层次博弈中独立选择使其效益最大化（或支付最小化）的基站。因此，该算法以分布式的方式实现了优化。...

Traditional remote sensing methods. AGs mapping has been widely concerned. Some pioneers extracted AGs from RS imagery by constructing novel spectral indices. Yang et al. (2017) proposed a new plastic greenhouse index PGI that can be used to identify transparent greenhouses in atmospherically corrected Landsat images; Guo and Li (2020) proposed a Normalized Difference Plastic Index NDPI using WorldView-3 SWIR bands; Zhang et al. (2022) constructed a novel spectral index APGI using Sentinel-2 images. However, these carefully constructed spectral indices may fail when the spectral response of AGs varies with seasons. Other works map AGs through an object-oriented approach.Wu et al. (2016) integrated RS data from Landsat-8 and an object-oriented classification method to implement an inheritance classification for AGs; Ji et al. (2019) proposed a threshold model created by 7 discriminative features to extract AGs from high-resolution GE imagery; Balcik et al. (2020) employed an object-based image classification method with 3 different classifiers to detect AGs in SPOT-7 and Sentinel-2 Multispectral Instrument (MSI) images. Although such methods are steady to the spectral features of AGs, they have limited feature extraction capabilities and are susceptible to isolated noise.

传统的遥感方法中，AGs的制图已经受到了广泛的关注。一些先驱利用构建新的光谱指数从RS图像中提取AGs。例如，Yang等人（2017）提出了一种新的塑料温室指数PGI，可用于在大气校正的Landsat图像中识别透明温室;...

1.Discuss how and why the SecurityManager class for JVM check methods can be defined differently based on context.. 2.The library class Collections contains a method sort with the following type signature. public static <T> void sort (T[] a, Comparator <? super T> c); The Comparator interface contains the following method which returns a negative integer, zero or a positive integer according to whether o1 is considered to be before, equal to or after o2 in a proposed ordering. int compare (T o1, T o2) ; What does the identifier T mean in the declaration of the method sort? Illustrate your answer with an example. 3.What does the notation <? super T> mean in the declaration of sort? Again, illustrate your answer with an example. 4.Explain the concept of an anonymous inner class in Java using a method call example. 5.Convert the method call example using an anonymous inner class to a lambda expression. Give the code required for this. 6.In what types of situations might you prefer to use an anonymous class instead of a lambda expression, and vice versa? 7.Explain (auto)boxing and (auto)unboxing in Java? 8.What does the annotation @Override mean in Java? What are the benefits of adding it? 9.What are wildcards in Java and how are they used in generic programming? 10.Can you explain the bounded wildcards, and provide an example

For example, if we have a class Animal and a subclass Dog, we can use a Comparator that accepts any superclass of Dog to sort an array of Dogs: Dog[] dogs = ...; Comparator<Animal> c = ...; ...

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ，建立了什么模型

这段文字并没有建立具体的模型，而是介绍了LyDROO算法的伪代码以及解决问题(P1)和(P4)的方法。该算法是一种在线学习算法，利用深度神经网络来近似最优策略，并通过解决最优资源分配问题来求解目标函数。...

As shown in Figure 1, the proposed network contains four strided convolutional layers and four strided deconvo- lutional layers. The Leaky Rectified Linear Unit (LReLU) with a negative slope of 0.2 is used after each convolutional and deconvolutional layer. The residual group [38] consists of three residual blocks, and 18 residual blocks are used in GRes. The filter size is set as 11×11 pixels in the first convo- lutional layer in the encoder module and 3 × 3 in all the other convolutional and deconvolutional layers. We jointly train the MSBDN and DFF module and use the Mean Squared Error (MSE) as the loss function to constrain the network output and ground truth. The entire training process con- tains 100 epochs optimized by the ADAM solver [28 ] with β1 = 0.9 and β2 = 0.999 with a batch size of 16. The initial learning rate is set as 10−4 with a decay rate of 0.75 after every 10 epochs. All the experiments are conducted on an NVIDIA 2080Ti GPU. The source code and trained models are availabe at https://github.com/BookerDeWitt/MSBDN- DFF 翻译

如图1所示，所提出的网络包含四个步幅卷积层和四个步幅反卷积层。在每个卷积和反卷积层之后使用LReLU（带有负斜率0.2的泄露整流线性单元）。残差组[38]包含三个残差块，GRes中使用18个残差块。在编码器模块的第一个...

将下面一段文字压缩到95-100词以内，不改变原有表达意思，并检查语法，使得其更符合学术论文表达规范：HMS faces the problem of narrow bandwidth and very challenging bandwidth expansion. With square periodic units, multiple resonance points face deterioration of transmission coefficients between them, preventing the formation of a continuous passband. The present In this paper, we investigate a method to achieve transmission coefficient improvement between resonance points using different period ratios as well as resonance point compression. And a multi-frequency phase compensation strategy for metasurface arrays based on ultra-wideband units is introduced in the array fabrication, which overcomes the inherent gain bandwidth attenuation and gain roll-off problems of lens antennas arranged only at the centre frequency. The designed HMS-L bandwidth is dramatically increased, achieving a 3-dB gain bandwidth of 49.84%, and an aperture efficiency of 50.7% is also achieved.

"The present In this paper, we investigate a method to achieve transmission coefficient improvement between resonance points using different period ratios as well as resonance point compression." -> ...

我正在写天线近场聚焦类的IEEE TAP文章，有一段话，请帮我检查语法问题，并指出错误和给出简短英文表述。Besides, the proposed method can also control multifocus and its unequal power distribution. Fig. \ref{Fig 3-2-2} shows the unequal power distribution result in the y = 0 plane. The FPs $\zeta_p$ are set to 1/6, 1/3 and 1/2, respectively. The $\epsilon_p$ are all set to 0.01. Fig. \ref{Fig 3-2-2}(b) shows three AFPs $\rho$ are -4.94 dB, -1.98 dB and -0.13 dB, respectively. It can be known that the proposed method achieve different power distribution relationship, which relationship with each AFP is approximately 1:2:3. Moreover, the FPs $\zeta_p$ are set to 1/5, 3/5 and 1/5, respectively. The $\epsilon_p$ are all set to 0.01. Fig. \ref{Fig 3-2-2}(b) shows three AFPs $\rho$ are -4.98 dB, -0.24 dB and -4.82 dB, respectively. It can be known that the proposed method achieve different power distribution relationship with each AFP is approximately 1:3:1. As the figure shows, the average focal shift using the proposed method is below 0.15$\lambda$, which is very similar to that the proposed single FP controlling method. Hence, this three examples prove that the proposed multifocus method is effective and its work performance excellent.

this three examples prove that the proposed multifocus method is effective and its work performance excellent.” 这里“that the proposed single FP controlling method”应改为“to that of the proposed ...

元模型驱动--KAYA低代码开发平台Java实现(一)

带你走进MDA的世界。－－真正的模型驱动开发。目前的建模工具很多，不过个人的观点来看，基本都跑偏了。没办法真正应用模型驱动来有效开发。废话少说。下面的就是MDA(KAYA)建模工具。左侧是需要用到的元素，简单说来包括 1.Product（产品&服务--可以看作系统名称）， 2.Role（角色--可以看作具体的业务表）， 3.Property（属性--表...

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