Dosimetric consequences of tumor volume changes after kilovoltage cone-beam computed tomography for non-operative lung cancer during adaptive intensity-modulated radiotherapy or fractionated stereotactic radiotherapy

Objective The aim of this study was to investigate tumor volume changes with kilovoltage cone-beam computed tomography （kV-CBCT） and their dosimetric consequences for non-operative lung cancer during intensity-modulated radiotherapy （IMRT） or fractionated stereotactic radiotherapy. Methods Eighteen patients with non-operative lung cancer who received IMRT consisting of 1.8-2.2 Gy/fraction and five fractions per week or stereotactic radiotherapy with 5-8 Gy/fraction and three fractions a week were studied, kV-CBCT was performed once per week during IMRT and at every fraction during stereotactic radiotherapy. The gross tumor volume （GTV） was contoured on the kV-CBCT images, and adaptive treatment plans were created using merged kV-CBCT and primary planning computed tomogra- phy image sets. Tumor volume changes and dosimetric parameters, including the minimum dose to 95% （D95） or 1% （D1） of the planning target volume （PTV）, mean lung dose （MLD）, and volume of lung tissue that received more than 5 （Vs）, 10 （Vl0）, 20 （V20）, and 30 （V30） Gy were retrospectively analyzed. Results The average maximum change in GTV observed during IMRT or fractionated stereotactic radio- therapy was -25.85% （range, -13.09% --56.76%）. The D95 and Dr of PTV for the adaptive treatment plans in all patients were not significantly different from those for the initial or former adaptive treatment plans. In patients with tumor volume changes of 〉20% in the third or fourth week of treatment during IMRT, adap- tive treatment plans offered clinically meaningful decreases in MLD and V5, V10, V20, and V30; however, in patients with tumor volume changes of 〈 20% in the third or fourth week of treatment as well as in patients with stereotactic radiotherapy, there were no significant or clinically meaningful decreases in the dosimetric parameters. Conclusion Adaptive treatment planning for decreasing tumor volume during IMRT may be beneficial for patients who experience tumor volume changes of 〉20% in the third or fourth week of treatment.

Stereotactic radiotherapy and the potential role of magnetic resonance-guided adaptive techniques for pancreatic cancer

BACKGROUND Pancreatic cancer is a malignancy with one of the poorest prognoses amongst all cancers.Patients with unresectable tumours either receive palliative care or undergo various chemoradiotherapy regimens.Conventional techniques are often associated with acute gastrointestinal toxicities,as adjacent critical structures such as the duodenum ultimately limits delivered doses.Stereotactic body radiotherapy(SBRT)is an advanced radiation technique that delivers highly ablative radiation split into several fractions,with a steep dose fall-off outside target volumes.AIM To discuss the latest data on SBRT and whether there is a role for magnetic resonance-guided techniques in multimodal management of locally advanced,unresectable pancreatic cancer.METHODS We conducted a search on multiple large databases to collate the latest records on radiotherapy techniques used to treat pancreatic cancer.Out of 1229 total records retrieved from our search,36 studies were included in this review.RESULTS Studies indicate that SBRT is associated with improved clinical efficacy and toxicity profiles compared to conventional radiotherapy techniques.Further dose escalation to the tumour with SBRT is limited by the poor soft-tissue visualisation of computed tomography imaging during radiation planning and treatment delivery.Magnetic resonance-guided techniques have been introduced to improve imaging quality,enabling treatment plan adaptation and re-optimisation before delivering each fraction.CONCLUSION Therefore,SBRT may lead to improved survival outcomes and safer toxicity profiles compared to conventional techniques,and the addition of magnetic resonance-guided techniques potentially allows dose escalation and conversion of unresectable tumours to operable cases.

Usability of Deformable Image Registration for Adaptive Radiotherapy in Head and Neck Cancer and an Automatic Prediction of Replanning

Deformable image registration (DIR) has been an important component in adaptive radiotherapy (ART). Our goal was to examine the accuracy of ART using the dice similarity coefficient (DSC) and to determine the optimal timing of replanning. A total of 22 patients who underwent volume modulated arc therapy (VMAT) for head and neck (H&N) cancers were prospectively analyzed. The planning target volume (PTV) was to receive a total of 70 Gy in 33 fractions. A second planning CT scan (rescan) was performed at the 15th fraction. The DSC was calculated for each structure on both CT scans. The continuous variables to predict the need for replanning were assessed. The optimal cut-off value was determined using receiver operating characteristic (ROC) curve analysis. In the correlation between body weight loss and DSC of each structure, weight loss correlated negatively with DSC of the whole face (rs = -0.45) and the face surface (rs = -0.51). Patients who required replanning tended to have experienced rapid weight loss. The threshold DSC was 0.98 and 0.60 in the whole face and the face surface, respectively. Patients who showed low DSC in the whole face and the face surface required replanning at a significantly high rate (P < 0.05 and P < 0.01). Weight loss correlated with DSC in both the whole face and the face surface (P < 0.05 and P < 0.05). The DSC values in the face predicted the need for replanning. In addition, weight loss tended to correlate with DSC. DIR during ART was found to be a useful tool for replanning.

RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.

Factors influencing comfort level in head and neck neoplasm patients receiving radiotherapy

Objective:To determine factors that influence comfort in head and neck neoplasm patients receiving radiotherapy.Methods:In total,200 head and neck neoplasm patients receiving radiotherapy were recruited from three tertiary first class hospitals.They were assessed by Radiotherapy Comfort Questionnaire for patients with head and neck neoplasm,Social Support Scale,and Medical Coping Modes Questionnaire.Results:The total score of comfort was 60.54±8.32.Multiple linear regression analysis indicated that number of radiation treatments,family accompaniment,educational level,resignation coping mode,complications due to diabetes,accompanying chemotherapy,and the utilization of social support significantly influenced comfort level(p<0.05).Among these,number of radiation treatments,complications due to diabetes,accompanying chemotherapy,and resignation coping were negative factors.Conclusion:Encouraging utilization of social support systems and a positive coping mode is important for increasing comfort level in head and neck neoplasm patients during radiotherapy.Nurses should pay particular attention to those patients during later stages of radiotherapy or chemotherapy,with diabetes,without family accompaniment,and with lower education level.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Adaptive Radiotherapy for Lung Cancer

The concept of adaptive radiotherapy(ART) was proposed 20 years ago, and since then a variety of methodologies and techniques have been developed to accommodate different clinical requirements, including both online and offline plan adaptations. Compared with pre-treatment planning, plan adaptation involves more computational tasks and consequently has increased complexity and computational burden. While ART can benefit many cancer patients, challenges still exist in development and implementation of high-quality ART programs. In this short review, we will focus on the development of offline ART for lung cancer. We will also discuss the advantages and disadvantages of different clinical implementations of ART.

An adaptive stable observer for on board auxiliary inverters with online current identification strategy

An adaptive stable observer with output current online identification strategy for the auxiliary inverters applied in advanced electric trains, such as high speed railway, urban rail, subway and maglev trains, is proposed. The designed observer is used to estimate the state variables, i.e. controllable duty ratio and current components in d-q-o rotary reference frame. The convergence of the observer estimation error is analyzed with consideration of uncertain level variation of input voltage at direct current(DC) side and sufficient conditions are given to prove its practical stability. Experimental results are shown to confirm the effectiveness of the proposed observer.

Study of Adaptive Learning for Online Education

Although the World Wide Web is now accessible almost everywhere, on - line instruction is not catching on so rapidly. In large part this is because courses must be assembled manually and cannot be adapted easily to individual student needs. The article points out that with the development of ww\v and computer technology, adaptive learning is necessary and possible for online education. Construction of adaptive program is described and some teaching strategies for adaptive learning is proposed.

Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected.Their network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural networks.In this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training RVFL.The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is analyzed.The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

Short-term traffic flow online forecasting based on kernel adaptive filter

Considering that the prediction accuracy of the traditional traffic flow forecasting model is low,based on kernel adaptive filter(KAF)algorithm,kernel least mean square(KLMS)algorithm and fixed-budget kernel recursive least-square(FB-KRLS)algorithm are presented for online adaptive prediction.The computational complexity of the KLMS algorithm is low and does not require additional solution paradigm constraints,but its regularization process can solve the problem of regularization performance degradation in high-dimensional data processing.To reduce the computational complexity,the sparse criterion is introduced into the KLMS algorithm.To further improve forecasting accuracy,FB-KRLS algorithm is proposed.It is an online learning method with fixed memory budget,and it is capable of recursively learning a nonlinear mapping and changing over time.In contrast to a previous approximate linear dependence(ALD)based technique,the purpose of the presented algorithm is not to prune the oldest data point in every time instant but it aims to prune the least significant data point,thus suppressing the growth of kernel matrix.In order to verify the validity of the proposed methods,they are applied to one-step and multi-step predictions of traffic flow in Beijing.Under the same conditions,they are compared with online adaptive ALD-KRLS method and other kernel learning methods.Experimental results show that the proposed KAF algorithms can improve the prediction accuracy,and its online learning ability meets the actual requirements of traffic flow and contributes to real-time online forecasting of traffic flow.

Forecasting-Based Adaptive Optimized Dispatch in Smart Grid Online

The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for the convenience of the implementation of adaptive control generators using advanced technologies. In this paper, we are introducing a new approach, the Central Lower Configuration Table, which optimizes dispatch of the generating capacity in a smart grid power system. The dispatch strategy of each generator in the grid is presented in the configuration table, and the scenario consists of two-level agents. A central agent optimizes dispatch calculation to get the configuration table, and a lower agent controls generators according to the tasks of the central level and the work states during generation. The central level is major optimization and adjustment. We used machine learning to predict the power load and address the best optimize cost function to deal with a different control strategy. We designed the items of the cost function, such as operations, maintenances and the effects on the environment. Then, according to the total cost, we got a new second-rank-sort table. As a result, we can resolve generator’s task based on the table, which can also be updated on-line based on the environmental situation. The signs of the driving generator’s controller include active power and system’s f. The lower control level agent carries out the generator control to track f along with the best optimized cost function. Our approach makes optimized dispatch algorithm more convenient to realize, and the numerical simulation indicates the strategy of machine learning forecast of optimized power dispatch is effective.

An adaptive control strategy for microgrid secondary frequency based on parameter identification

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy.

论网络文学改编类古装剧的“想象力消费”:现象、本质与反思

网络文学改编类古装剧是近年中国剧集生态中的突出现象,此类改编剧降解了主流话语主导下“再现”历史的“正史”表达功能,加强了大众文化语境中影视剧的文化消费功能,尤其体现在“网生代”对历史可“穿越”、拼贴、杂糅、并置的想象上。这是一种具有明显青年亚文化特性的对历史的“想象力消费”。叙事游戏化与性别想象多元化是此类古装剧在环境、人物、时空设置、性别想象上体现出的新变化。结合大塚英志、东浩纪的二次元文化研究,可以认为这一变化实则表明“想象力消费”已经成为网络文学改编类古装剧乃至网络文化生产与消费的重要原则。但网络文学改编类古装剧创作存在不少问题,应该调整策略,优化质量,以适应在当下文化语境中的生存与发展。

基于慕课的肿瘤放射治疗学线上线下混合教学模式的应用

目的探讨肿瘤放射治疗学基于大规模在线开放课程(简称慕课)的线上线下混合教学模式的应用效果。方法:以2017—2019级蚌埠医学院医学影像学专业本科生为研究对象,对其分别采用单纯线上、单纯线下和基于慕课的线上线下混合三种方式进行肿瘤放射治疗学的教学活动,通过分析学生客观考试成绩和主观满意度,比较三种不同教学模式的效果有无差异。结果:肿瘤放射治疗学的三种不同教学模式之间,学生的客观考试成绩和主观学习积极性、学习效率、思辨能力、以及教学互动的各项满意度均具有统计学差异(P<0.05),其中基于慕课的线上线下混合教学模式的考试成绩和满意度最高。结论:基于慕课的线上线下混合教学模式能够提高肿瘤放射治疗学的教学效果,值得推广。

“女性向”网络文学影视改编的影响及优化策略

网络文学在为影视改编提供了丰富内容和题材的同时,也拓展了自身的发展空间。规模不断扩大的“女性向”网络文学影视改编展现了当代女性自我主体意识的觉醒和审美倾向的转变,其在提升网络文学商业价值的同时,也产生了内容脱离原有语境、故事内核单一、现实观照不足等问题。当下,“女性向”网络文学影视改编应尊重原著、合理改编,正确诠释女性主义,传递正向的价值观,推动网络文学与影视融合高质量发展。

基于镜像修正FxLMS控制算法的船舶管路振动主动控制

针对船舶管路减振和抗冲击的需求,本文根据镜像修正自适应滤波算法,设计出了一种管路振动主动控制策略,能够有效地控制管路在低频下的振动,并且在次级通道发生突变时,控制系统可再次快速收敛,进行稳定控制。本文先对镜像修正自适应滤波算法进行理论研究,分析算法的迭代及控制过程;再通过仿真分别验证算法在不同参考信号输入下的收敛性及稳定性;最后搭建实验台架,通过试验验证算法的实际控制效果。试验结果表明:该控制策略在管路振动主动控制中能够降低15.37%的振动强度,比自适应滤波算法控制策略的控制效果好8.85%。所以镜像修正自适应滤波算法能够及时有效地进行管路振动控制。

武器站伺服传动装置在线辨识与自适应控制方法

针对武器站俯仰轴伺服传动装置在武器载荷变化时控制精度和安全性下降的问题,提出基于多参数在线辨识的自适应复合控制方法。基于符号函数的线性化方法,构建包含传动间隙、电机和负载摩擦非线性的伺服传动装置待辨识模型框架,推导出待辨识参数的显式迭代方程,通过带遗忘因子的递推增广最小二乘算法实现参数在线辨识。在此基础上,提出自适应比例积分控制器与自适应状态扩张卡尔曼滤波器相结合的复合控制方法。实验结果表明:参数辨识方法可实现伺服传动装置9个关键动力学参数的精确在线辨识,稳态辨识误差不超过10%。自适应复合控制方法将系统速度跟随残差均方根降低了28.24%,有效提升了俯仰轴在武器载荷变化时的控制精度和稳定裕度。