Long-term assessment of collagenase treatment for Dupuytren’s contracture:A 10-year follow-up study

BACKGROUND Enzymatic fasciotomy with collagenase clostridium histolyticum(CCH)has revolutionized the treatment for Dupuytren’s contracture(DC).Despite its benefits,the long-term outcomes remain unclear.This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.AIM To compare the short-term(12 wk)and long-term(10 years)outcomes on CCH treatment in patients with DC.METHODS A cohort of 45 patients was treated with CCH at the metacarpophalangeal(MCP)joint and the proximal interphalangeal(PIP)joint and underwent systematic reevaluation.The study adhered to multicenter trial protocols,and assessments were conducted at 12 wk,7 years,and 10 years post-surgery.RESULTS Thirty-seven patients completed the 10-year follow-up.At 10 years,patients treated at the PIP joint exhibited a 100%recurrence.However,patients treated at the MCP joint only showed a 50%recurrence.Patient satisfaction varied,with a lower satisfaction reported in PIP joint cases.Recurrence exceeding 20 degrees on the total passive extension deficit was observed,indicating a challenge for sustained efficacy.Significant differences were noted between outcomes at the 7-year and 10-year intervals.CONCLUSION CCH demonstrated sustained efficacy when applied to the MCP joint.However,caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction.Re-intervention is needed within a decade of treatment.

Granular Cell Tumor of the Esophagus: A Patient Treated by Endoscopic Mucosal Resection with Long Term Follow-Up

Granular cell tumors (GCTs) of the esophagus are uncommon. We report a case of granular cell tumor of esophagus treated by endoscopic mucosal resection (EMR) with long term follow-up.

Long Term Follow-Up Results of a Fractional Radio Frequency Ablative Treatment of Acne Vulgaris and Related Acne Scars

Introduction: Acne vulgaris and acne scarring are prevalent conditions that can have a negative effect on a patient’s quality of life. Fractional radiofrequency technologies have been shown to be clinically safe and effective in managing acne scars through dermal remodeling without causing direct damage to the epidermis. In a recently published study, we presented our clinical and histological results in the treatment of patients with active acne and acne related scarring using a Fractional RF (FRF) device. In the current article we demonstrate long term follow-up results, up to two years post last fractional treatment. Methods: Four out of the eight patients who completed a four treatment regimen were invited for long term follow-up visit to document treatment results. In some cases, touch-up treatments were conducted to optimize clinical results. Results: Patients demonstrated significant improvement of acne lesions, acne scarring, pores and skin texture. Long term photos demonstrated that clinical improvement progressed with time. Conclusion: The current study further supports the previous findings that FRF is a safe and effective treatment modality for active acne and acne related scars. Treatment protocol can be customized according to patient needs and clinical results last for long term.

Stroke: Medium and long-term mortality and associated factors in French-speaking West Africa, case of Benin

Introduction: Stroke is the leading cause of mortality and physical disability in sub-Saharan Africa. Objective: Determining medium-term and long-term mortality for stroke and identifying associated factors. Method: It consists in a cross-sectional, prospective, descriptive and analytical study that was conducted from April 1 to August 31, 2013 in the Neurology Department of CNHU-HKM in Cotonou. It involved patients who have known stroke for at least 6 months, and were all admitted and discharged later on. The disease survivors were re-contacted and examined again at home or at hospital. Then, the number of deceased was systematically recorded with precision of death time-limit. Results: The overall mortality rate was 29%. Mortality was higher with patients over 70 years with a frequency of 57.1%. The medium-term mortality rate was 25% against 4% for long-term. The average time-limit for death occurrence after the vascular incident was 7 months ± 6.4 months. Prognostic factors of mortality were: the age of the patient (IC95% = 7.73 [1.49 - 39.99], p = 0.015 ), marital status (IC95% = 0.27 [0.08 to 0.94], p = 0.039 ) and the presence of aphasia (IC95% = 5.52 [1.45 to 20.94 ], p = 0.012). Conclusion: Stroke mortality still remains significant, even after the patients have been discharged from hospital. A good psychological family support and efficient aphasia coverage are essential for its reduction.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

基于多模型融合的中长期径流集成预测方法

中长期水文预报是流域水资源规划与合理配置的重要依据。为提高中长期径流预测精度,提出了一种基于多模型融合的水库中长期径流集成预测方法。该方法将ARMA、BP、LSTM、RF和SVR等5个异质预测模型进行融合,同时采用超参数优化方法确定各模型的最优参数。将其用于青海省龙羊峡水库的中长期径流预报中,结果表明,通过Stacking融合算法建立的集成预测模型相较于单一模型,取得了更高的预测精度(R2值由0.71提升至0.82)。此方法可为提升流域中长期径流预测精度提供一定参考。

基于可解释机器学习的黄河源区径流分期组合预报

黄河源区是黄河流域重要的产流区和我国重要的清洁能源基地,提高黄河源区径流预报准确率可为流域水资源科学调配和水风光清洁能源高效利用提供重要支撑。以黄河源区唐乃亥和玛曲水文站为研究对象,基于不同月份径流组分的差异,考虑积雪覆盖率及融雪水当量变化,构建了中长期径流分期组合机器学习预报模型及其可解释性分析框架。研究结果表明:1)年内的径流预报时段可划分为融雪影响期(3—6月)和非融雪主导(以降雨和地下水补给为主)期(7月—次年2月);2)与传统不分期模型相比,唐乃亥站和玛曲站分期组合预报模型的纳什效率系数分别达0.897、0.835,确定系数(R2)分别达0.897、0.839,均方根误差分别降低了10%、17%,提高了径流预报准确率,通过分位数映射校正,唐乃亥站和玛曲站预报模型的R2分别进一步提升至0.926和0.850;3)基于SHAP机器学习可解释性分析框架,辨识了预报因子对径流预报结果的贡献程度,由高到低依次为降水、前一个月流量、蒸发、气温、相对湿度、融雪水当量等,发现了不同预报因子之间交互作用散点分布具有拖尾式或阶跃式的特征。

基于集合Kalman滤波的中长期径流预报

为降低中长期径流预报的不确定性,增加水电站水库的发电效益,针对现有方法侧重于提高单一预报模型确定性预报结果的准确性以降低径流预报不确定性的问题,提出一种基于集合Kalman滤波的入库径流确定性预报方法。以旬为预见期的锦西水库实例验证结果表明:相比传统的单一预报模型和传统的信息融合预报模型,基于集合Kalman滤波的中长期径流预报可使RMSE降低4.78 m^(3)/s,合格率可提高0.56%,且更有效地降低了汛期预报的不确定性,得到了更加准确、可靠的确定性径流预报结果,可为开展流域梯级水电站优化调度提供技术支持。

基于双重分解和双向长短时记忆网络的中长期负荷预测模型

针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。

基于长短时记忆网络的山区中小流域降雨径流模拟

洪水预报是流域防洪减灾的重要非工程措施之一。目前我国中小河流暴雨洪水灾害频发,但应对短历时强降雨的洪水预报能力仍不强。以安徽省东部山区中小流域为研究对象,引入长短时记忆网络建立流域降雨径流模型,探讨其在山区中小流域的洪水模拟效果。结果表明,考虑降雨输入的空间差异可提升深度学习模型降雨径流模拟预测性能,且长短时记忆网络能够取得优于传统人工神经网络的精度;长短时记忆网络模型有效建立了流域降雨与径流间的复杂非线性关系,模型在所选流域内场次洪水的峰值模拟效果较好,训练、测试集场次洪水峰值合格率均在90%以上;长短时记忆网络内部结构特征与流域水文过程具有较好的相似性,对山区中小流域暴雨洪水非线性关系拟合效果突出。

考虑发用电相似性的海上风电中长期双边协商交易优化决策模型

海上风电是未来最有潜力的可再生能源之一,但其出力具有随机性和波动性。为了更好地促进海上风电的市场化消纳,文中基于海上风电商与负荷聚合商间的中长期双边协商交易构建了优化决策模型。首先,通过时间序列相似性评估方法,为目标海上风电寻找最优的用电负荷组合。其次,考虑需求响应备用容量配置和发用电曲线预测误差,构建了基于两阶段分布鲁棒优化的中长期交易优化决策模型,为海上风电配置适应其未来一段时间内出力特性的需求响应资源,并合理调整中长期交易曲线。最后,通过仿真算例验证了所提模型的有效性和实用性。

基于多因子多模式集成的中长期径流预测模型

提高中长期径流预测精度对于水资源调度等具有重要意义和应用价值。基于国家气候中心的130项气候因子,采用皮尔逊相关系数、最大信息系数、方差增量指标筛选主要预测因子,建立基于DS(Dempster-Shafer)证据理论的多因子综合方法;采用随机森林、BP神经网络和贝叶斯网络等建立基于水文-气象因子遥相关的中长期径流预测模型,构建基于DS证据理论的预测结果集成模型。以三峡水库为对象开展实例研究,结果表明:引入遥相关因子能有效提高预测精度;基于DS证据理论的多因子综合方法能筛选出综合性更强、稳定性更优的因子,弥补单一筛选方法的不足;基于DS证据理论的多因子多模式集成方法在径流预测精度上优于单一方法单一模型,确定性系数提高到0.823,平均相对误差降低到23.2%。

护航战略目标落地:澳大利亚国家教育监测评估的经验与启示

开展针对国家中长期教育政策性宣言的教育监测评估是澳大利亚推进教育改革发展的有效经验,包括:以法律和政策坚实监测评估“合法性”基础,依据政策性宣言设计监测指标体系,打造支撑监测评估的多源异构数据基座,以“见林见树”的全面观呈现现状与发展趋势,强化监测评估服务行政决策和公共监督。澳大利亚的经验对我国开展国家教育监测评估有如下启示:一是政府委托专门机构对国家教育中长期战略落实进展监测评估,二是监测评估坚持纵向发展与横向国际比较相结合,三是强化监测评估服务政府决策和舆论引导效应,四是推动人工智能等新一代信息技术与教育监测评估深度融合。

考虑多能源同台竞价的电力中长期融合交易机制设计与实践

新能源的入市可加速新型电力系统建设,针对新能源发电的随机性和波动性而导致上网电量与市场交易电量偏差较大的问题,设计了一种多能源同台竞价的融合交易机制。通过设计融合交易机制的组织流程与出清方式构建了新能源发电商(NEP)在不同交易模式下的结算模型,以江西电力中长期市场为背景设置算例参数,对NEP在几种交易模式下的收益进行分析,并对采用融合交易机制的江西电力中长期市场运行情况进行了对比分析。结果显示所提交易机制能促进新NEP达成交易,明显提升NEP的经济效益,有效推动新能源的入市进程并显著减少交易偏差率。

基于STE-TCN的中短期电力负荷预测

目的 针对传统电力负荷预测模型对长序列预测精度低的问题,提出一种结合跳级卷积连接与时间编码网络的新型时序卷积神经网络(TCN)模型——STE-TCN模型。方法 首先对TCN模型加入跨周期的膨胀卷积通道(Skip-convolution)提取电力数据周期信息;再进行特征融合得到Skip-TCN网络,使网络抓取周期规律,增加信息利用长度;最后设计日期编码网络(Time encoding network)捕捉生活周期和季节性特征,与Skip-TCN进行特征融合得到STE-TCN模型,实现对电力负荷数据长序列预测。结果 实验表明:在与TCN模型和传统时序网络的对比下,Skip-TCN的预测精度均有提升,在预测长度更长的测试上提升尤为明显。结论 实验结果验证了通过对更长跨度时序关系的捕捉,STE-TCN网络改进方法有效提升了对长序列电力负荷的预测精度。

基于多模型比选耦合的降水预测

变化环境下水文时间序列的模拟预测难度不断加大,以往研究大多聚焦在模型的不同组合尝试与应用探索,但缺乏针对不同组合模型适用性与稳定性的系统研究。选择经验模态分解(empirical mode decomposition,EMD)、总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)、改进的总体平均经验模态分解(modified ensemble empirical mode decomposition,MEEMD)和变分模态分解(variational mode decomposition,VMD)4种常用的分解算法,与多元线性回归(multivariable linear regression,MLR)、随机森林(random forest,RF)、BP神经网络(back propagation,BP)、卷积神经网络(convolutional neural networks,CNN)和长短期记忆神经网络(long short-term memory,LSTM)5种具有代表性的模型结合,构建20种基于“分解-预测-重构”模式的组合模型,并以华北地区密云、官厅两流域年和汛期降水为例,进行模型适用性与稳定性综合对比分析。结果表明:单一模型对密云流域年降水和汛期降水的预测结果优于官厅流域,但整体预测结果均不理想;结合分解算法后的组合模型预测结果明显优于单一模型,且该预测结果存在正负误差抵消现象,因此有助于进一步提高组合模型的整体预测精度;与基于EMD系列的分解算法相比,VMD算法对模型预测精度提升效果最显著,组合模型适用性和稳定性整体上表现为VMD-MLR>VMD-LSTM>VMD-BP>VMD-CNN。

国家节水中长期规划编制思路浅析

近二十年来,我国相继印发实施多个节水规划,指导和推动节水工作取得显著成效。科学编制节水中长期规划,系统谋划未来一个时期节水工作推进的战略方向、总体布局、重点任务等,有利于全面提升我国水资源节约集约利用水平、加快推进节水型社会高质量建设。总结我国节水规划实践情况,评估规划目标制定及其完成情况,分析我国现状用水节水水平,面向基本实现现代化,对标新质生产力和水利高质量发展要求,提出节水中长期规划编制的总体思路,梳理重点任务,包括摸清节水现状与形势需求,制定节水目标指标体系,谋划节水重大战略布局,提出重点节水行动与举措,梳理重点工程项目和实施计划,完善体制机制和制度政策保障等,针对性提出推进规划编制工作的有关建议。

兼顾激励相容的水电富集电网中长期电力市场交易模型

针对我国水电富集地区梯级电站上下游中标电量与发电水量不匹配导致弃水和违约等问题,借鉴巴西电力市场中长期交易模式,提出兼顾激励相容的水电富集电网中长期交易模型。该模型以个体发电效益最大和电网侧购电成本最小作为激励相容的双重目标。首先以最小出力最大为目标对水电站群进行优化调度,所得各水电站每月发电量定义为其该月保证电量,并将保证电量拆分成基础电量、省内电量和西电东送电量。然后建立各级市场交易方法,基础电量按相似来水频率的历年平均电价收购,省内电量参与两轮拍卖,西电东送电量按剩余电量比重排序依次收购,采用强化学习确定各发电商份额和电价。最后利用改进的电量再分配机制对联盟内各水电站实发电量进行再分配。云南省5个流域52座电站群实例结果表明,所提模型能有效缓解当前梯级上下游电站弃水和违约等问题,促进了水电市场合理竞争和水资源充分利用。

考虑上游来水影响的中长期径流预报

雅砻江流域地面气象站点不足、分布不均,难以获得精确的流域面降雨资料,加之传统中长期径流预报模型泛化能力有限,中长期径流预报存在较大瓶颈。充分考虑流域水库间的物理联系,基于上下游水库流量变化在时空上的相似性,对1957年~2020年锦屏一级水库和二滩水库的历史月径流数据进行主成分分析,使用BP人工神经网络、随机森林和支持向量回归3种机器学习方法建立3种径流预报模型,通过决定系数R^(2),合格率Q R以及平均相对误差MRE三项指标构成的评价体系对预测结果进行评估。结果表明,上游水库对于下游水库的入库流量具有显著影响,且3种模型在二滩水库中长期径流预报上均具有较好的预报效果(R^(2)>0.8、Q R>0.7、MRE<0.2)。随机森林模型模拟效果整体优于BP人工神经网络和支持向量回归模型,3种模型均具有较好的实用性,能为流域水资源精细化调度及科学管理提供数据基础。

基于历史天气的区域电网负荷预测

随着社会经济的迅速发展,人们对电能的需要日益增加,但是在电网运行中,常常会出现电力产能过剩或者不足的情况,为保证电力系统安全稳定、经济运行,就必须掌握各种区域电网负荷的变化规律和发展趋势。论文对重庆市区供电分公司供电区域电网中长期负荷进行预测,提出一种预测区域电网中长期负荷的方法,即一种基于前12个月历史天气条件和区域电网负荷关联关系的多元非线性拟合的特征参数因子曲线的中长期负荷预测方法,建立基于不同算法的多种预测模型,通过归一化处理,得到的区域电网中长期负荷预测的精度高,与实际区域电网负荷之间的误差小,对于区域电网中长期负荷预测分析具有重要参考利用价值。