胸腺瘤放疗的相关定义和报告指南

放疗在胸腺肿瘤的治疗中仍然占有重要地位，但是很多细节没有明确的定义。例如，如何放疗、如何确定放疗范围、哪些患者需要放疗和如何报告放疗结果都没有统一的标准。ITMIG的成立，给解决这些问题创造了机会。但是先决条件是制定统一的定义和方法，使不同结果能被理解和比较，这也是本文的主旨。文献和形成初步建议，再提交给下一个扩展工作组（Charles hTomas, Lynn Wilson, Gregory Videtic. James Metz, Harun Badakhshi, Clifton Fuller, Franc-oie Mornex, Conrad Falkson, David Ball, and Ken Rosenzweig）审议，之后将建议提交给ITMIG多学科组进一步讨论，形成初稿（包含推荐方法和标准操作流程）后分发给所有ITMIG成员更进一步讨论并反馈意见，最终版本经ITMIG批准并被采用。

胸腺恶性肿瘤化疗的相关定义和策略

胸腺恶性肿瘤是少见的上皮源性肿瘤，但部分肿瘤侵袭性强且治疗效果不佳[1]。胸腺瘤多发于前纵隔，手术切除是主要的根治性治疗方式[1]。然而，30%的患者确诊时即为进展期胸腺瘤，包括侵犯邻近脏器，向胸膜、心包播散，以及胸腔外脏器的转移。对于进展期胸腺瘤，化疗有两个明确目的，其一是降低肿瘤负荷为后续手术或放疗创造机会，其二是延长疾病的控制时间。对于术后复发可以采取相同的化疗策略。尽管胸腺癌发病率很低，但诊断时多已是晚期，全身性治疗显得尤为重要。

富血小板血浆注射：慢性盘源性腰痛的新疗法

研究表明,自体富血小板血浆(platelet-rich Plasma, PRP)注射是治疗包括退行性腰椎间盘疾病在内的多种肌肉骨骼疾病的一种新方法。虽然医师越来越关注PRP,但其临床疗效只有对膝骨性关节炎和肱骨上髁炎的报道。尽管PRP在退行性椎间盘疾病中应用普遍,但仍然需要可靠的临床证据证明其实用性和有效性。该文回顾了现有PRP疗法的文献及其治疗慢性椎间盘源性腰痛(简称盘源性腰痛)的潜在用途,并重点关注其临床试验的证据。

Deployment and demonstration of wide area monitoring system in power system of Great Britain

The creation of a suitable wide area monitoring system(WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure,efficient and sustainable for the foreseeable future. In Great Britain(GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring(VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Design rules for a tunable merged-tip microneedle

This publication proposes the use of an elasto-capillarity-driven self-assembly for fabricating a microscale merged-tip structure out of a variety of biocompatible UV-curable polymers for use as a microneedle platform.In addition,the novel merged-tip microstructure constitutes a new class of microneedles,which incorporates the convergence of biocompatible polymer micropillars,leading to the formation of a sharp tip and an open cavity capable of both liquid trapping and volume control.When combined with biocompatible photopolymer micropillar arrays fabricated with photolithography,elasto-capillarity-driven self-assembly provides a means for producing a complex microneedle-like structure without the use of micromolding or micromachining.This publication also explores and defines the design rules by which several fabrication aspects,such as micropillar dimensions,shapes,pattern array configurations,and materials,can be manipulated to produce a customizable microneedle array with controllable cavity volumes,fracture points,and merge profiles.In addition,the incorporation of a modular through-hole micropore membrane base was also investigated as a method for constitutive payload delivery and fluid-sampling functionalities.The flexibility and fabrication simplicity of the merged-tip microneedle platform holds promise in transdermal drug delivery applications.

Analysis and Control of MVDC Demonstration Project in the UK:ANGLE-DC

The emerging medium voltage direct current(MVDC)distribution networks are becoming more attractive due to their flexible power flow control and lower losses compared to traditional AC networks.This will significantly increase the wide uptake of renewable energy sources.The optimum utilization of the existing assets is an important aspect in grid upgrading and planning.One feasible option is to convert existing MVAC lines into MVDC operation.One of the practical demonstrations is the“ANGLE-DC”project which is also the first MVDC link in the UK.This paper highlights the innovative approach,challenges and key benefits delivered by the ANGLE-DC project.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.