Medium-term surgical outcomes and health-related quality of life after laparoscopic vs open colorectal cancer resection: SF-36 health survey questionnaire

BACKGROUND Previous studies that compared the postoperative health-related quality of life(HRQoL)outcomes after receiving laparoscopic resection(LR)or open resection(OR)in patients with colorectal cancer(CRC)have different conclusions.AIM To explore the medium-term effect of postoperative HRQoL in such patients.METHODS This study randomized 567 patients undergoing non-metastatic CRC surgery managed by one surgeon to the LR or OR groups.HRQoL was assessed during the preoperative period and 3,6,and 12 mo postoperative using a modified version of the 36-Item Short Form(SF-36)Health Survey questionnaire,emphasizing eight specific items.RESULTS This cohort randomly assigned 541 patients to receive LR(n=296)or OR(n=245)surgical procedures.More episodes of postoperative urinary tract infection(P<0.001),wound infection(P<0.001),and pneumonia(P=0.048)were encountered in the OR group.The results demonstrated that the LR group subjectively gained mildly better general health(P=0.045),moderately better physical activity(P=0.006),and significantly better social function recovery(P=0.0001)3 mo postoperatively.Only the aspect of social function recovery was claimed at 6 mo,with a significant advantage in the LR group(P=0.001).No clinical difference was found in HRQoL during the 12 mo.CONCLUSION Our results demonstrated that LR resulted in better outcomes,including intra-operative blood loss,surgery-related complications,course of recovery,and especially some health domains of HRQoL at least within 6 mo postoperatively.Patients should undergo LR if there is no contraindication.

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNNMedium-term forecast of dailypassenger volume of high speedrailway based on DLP-WNN

Purpose – This paper aims to propose a medium-term forecast model for the daily passenger volume of HighSpeed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume formultiple consecutivedays (e.g. 120 days).Design/methodology/approach – By analyzing the characteristics of the historical data on daily passengervolume of HSR systems, the date and holiday labels were designed with determined value ranges.In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double LayerParallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of thedaily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result byweighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of dailypassenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume toensure the accuracy of medium-term forecast.Findings – According to the example application, in which the DLP-WNN modelwas used for the medium-termforecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the averageabsolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP)neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalizedregression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for themedium-term forecast of the daily passenger volume of HSR.Originality/value – This study proposed a Double Layer Parallel structure forecast model for medium-termdaily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and WaveletNeural Network. The predict results are important input data for supporting the line planning, scheduling andother decisions in operation and management in HSR systems.

Studies on hybridization effects of different geographic populations of Chlamys farreri Ⅱ.The medium-term growth and development of Chlamys farreri populations from China and Russia and their reciprocal crosses

Based on the research of juvenile (2, 3, 4 months) growth and survival of three populations of two different geographic areas in Chlamys farreri from Russian and China and their F1 hybrids derived from Chinese cultural population (CC) (?) × Russian population (RW) (?) , Chinese wild population (CW) (?) × Russian population (RW) (?), Russian population (RW) (?) × Chinese wild population (CW) (?) , the study of the medium-term (6, 8, 10, 12 months) growth and development of Chlamys farreri was carried out. The four determined results indicated that there existed different extent heterosis (3% -52%) for the growth in three types of F1 hybrids, and the offspring derived from CC(?) ×R(?) had a stronger heterosis among the crosses at the medium-term; the uptrend among traits are wet weight >shell width>shell length> shell height, Chinese cultural population could be recognized as excellent parent, and seasonal variations influence very much on the daily increment and growth rate of each trait of Chlamys farreri and it is only able to survive and could barely grow in winter (6-8 months), but grows fast in temperate season (10-12 months).

Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression

Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose a new methodol-ogy that uses hourly daily loads to predict the next year hourly loads, and hence predict the peak loads expected to be reached in the next coming year. The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results obtained using the pro-posed methods showed that their performance is close and they outperform results obtained using the widely used ex-ponential regression technique. Moreover, peak load prediction has about 90% accuracy using the proposed method-ology. The methods are generic and simple and can be implemented to hourly loads of any power system. No extra in-formation other than the hourly loads is required.

The Relationship Between Abnormal Meiyu and Medium-Term Scale Wave Perturbation Energy Propagation Along the East Asian Subtropical Westerly Jet

The East Asian subtropical westerly jet(EASWJ)is one of the most important factors modulating the Meiyu rainfall in the Yangtze-Huaihe River Basin,China.This article analyzed periods of the medium-term EASWJ variation,wave packet distribution and energy propagation of Rossby waves along the EASWJ during Meiyu season,and investigated their possible influence on abnormal Meiyu rain.The results showed that during the medium-term scale atmospheric dynamic process,the evolution of the EASWJ in Meiyu season was mainly characterized by the changes of3-8 d synoptic-scale and 10-15 d low-frequency Rossby waves.The strong perturbation wave packet and energy propagation of the 3-8 d synoptic-scale and 10-15 d low-frequency Rossby waves are mostly concentrated in the East Asian region of 90°-150°E,where the two wave trains of perturbation wave packets and wave-activity flux divergence coexist in zonal and meridional directions,and converge on the EASWJ.Besides,the wave trains of perturbation wave packet and wave-activity flux divergence in wet Meiyu years are more systematically westward than those in dry Meiyu years,and they are shown in the inverse phases between each other.In wet(dry)Meiyu year,the perturbation wave packet high-value area of the 10-15 d low-frequency variability is located between the Aral Sea and the Lake Balkhash(in the northeastern part of China),while over eastern China the wave-activity flux is convergent and strong(divergent and weak),and the high-level jets are strong and southward(weak and northward).Because of the coupling of high and low level atmosphere and high-level strong(weak)divergence on the south side of the jet over the Yangtze-Huaihe River Basin,the low-level southwest wind and vertically ascending motion are strengthened(weakened),which is(is not)conducive to precipitation increase in the Yangtze-Huaihe River Basin.These findings would help to better understand the impact mechanisms of the EASWJ activities on abnormal Meiyu from the perspective of medium-term scale Rossby wave energy propagation.

Seismicity Quantification and Its Application to Medium-term Earthquake Prediction

Bed on the analysis of each parameter describing seismicity,we think A(b)-value can betterquantitatively describe the feature of the enhancement and quietness of seismicity in this paper. Thedata of moderate or small earthquakes during 1972～1996 in North China are used in space scanningof A(b)-value. The result shows that 2～3 years before most strong earthquakes there wereObviously anomaly zones of A(b)-value with very good prediction effect. Some problems about themedium-term prediction by using A(b)-value are also discussed.

Determination of Regions With Medium-Term Risk of Strong Earthquakes: Pre-warning Active Faults

To predict the area with frequent seismicity and the future risky region of strong earthquakes on the time scale of one or several years is a very important and urgent problem that needs to be solved.On the basis of active fault research,pre-warning active faults that have been active recently will be discussed; then the medium-term risky region of strong earthquakes will be delimited around the pre-warning active faults.This method proves to be effective.

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Medium-term air quality assessment,benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes.By using daily and monthly averaged data,medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives.By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark(CLAB).The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator.Such a benchmark can provide an overall air quality assessment for areas of interest.A case study is presented for Dallas County(U.S.A.)applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019.Six air pollutants considered are:ozone,carbon monoxide,nitrogen dioxide,sulfur dioxide,benzene and particulate matter less than 2.5 micrometres.These pollutants are assessed individually and in terms of CLAB,and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019.Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years.The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs.Conducting CLAB analysis on an ongoing basis,using a relevant near-past time interval for benchmarking that covers several years,can reveal useful monthly,seasonal and annual trends in overall air quality.This type of medium-term,benchmarked air quality data analysis is well suited for ecosystem monitoring.

ANALYSES OF ERRORS IN MEDIUM-TERM NUMERICAL FORECAST PRODUCTS FOR THE SUBTROPICAL HIGH 1998

By statistic and dynamic analyses, we have come to the following conclusions: (1) The ECMWF medium-term numerical forecast can forecast medium-term activity of subtropical high, and the accuracy rate of forecast cannot have large improvement by translational corrections. (2) The important cause for the ECMWF medium-term numerical forecast to have errors in 1998 is that the astronomical tide is not included in the model. (3) Two indexes are found from which it can be judged that ECMWF medium-term numerical forecast will have errors if the astronomical tide is ignored in the model : ① When the 54.7?line under the moon of the nodical month astronomical singularities coincides with the trough-line of the subtropical jet flow from 50癊 to 150癊 on the 500 hPa level at 2000 L.T. of the same day, and is approximately vertical (α>60? with the isotherm, then the day 0 2 days after the appearance of the nodical month astronomical singularities is defined as the initial day. Then in three successive days after the initial day, ECMWF medium-term numerical forecast of the northern latitude of the 588 line at 120 癊 will have continuous errors as large as two latitudes (7/9). Otherwise, it won’t have continuous errors (13/13). ② Otherwise, if the 54.7 ?line is in the range of a low pressure between two high pressures, then there is a dispersive error on the day of the nodical month astronomical singularities (5/7). There is not any error (6/6) otherwise.

Total Hip Replacement: Short- and Medium-Term Morbidity and Mortality in the Department of Orthopedic Traumatology of Donka University Hospital, Guinea-Conakry

Introduction: Total hip prostheses are a reliable means of treating hip disorders. It is indicated when pain and reduced mobility of the hip become incompatible with the patient’s daily activities. In low-income countries, the risk factors for hip disease are numerous, but the means for its management, such as total hip prostheses, are not. The aim of this work was to determine the morbi-mortality of total hip prostheses in the short and medium term in our department and to review the literature. Patients and Method: This was a prospective and descriptive study over a period of 9 years. It involved 50 THPs performed on 45 patients. Patients who had received a THP and were followed up for at least two years were included. We excluded patients who were lost to follow-up. The Moore-type posterolateral minimally invasive approach was used. Complications were investigated from the immediate postoperative period and in the medium term. The final functional results were evaluated according to the Postel Merle d’Aubigné score. Results: We performed 50 THPs out of 750 procedures, i.e. , 6.6%. The indications were: primary coxarthrosis 60%, necrosis of the femoral head 26%, post-infectious balloon hip 4%, and pseudarthrosis of the femoral neck 10%. The average time to the consultation was 2.4 years with extremes of 1 and 5 years. According to the terrain, sickle cell disease represented 18%, tuberculosis 12%. Postoperative complications were lameness 12%, dislocation 6%, suppuration 6%, death 4%, peri-prosthetic fracture 8%, loosening 2%, peri-articular ossification 2%, and paralysis of the external popliteal nerve 2%. Conclusion: Whoever performs a THP is obliged to monitor the patient as long as he/she is alive. Complications are possible at any time and can negatively change the assigned goal and force the surgeon to be expensive and sometimes inconclusive secondary interventions.

基于概率增益模型的强震概率预测研究

为提高云南地区及邻区强震中短期预测的准确度,通过强震发生前不同地震前兆指标的对比研究,发现仅用单项指标很难提高预测的准确度。挑选出能从不同侧面反映强震发生规律的6项指标,采用概率增益模型对这些指标进行综合处理,通过估算每种地震前兆的概率增益来定量判定地震发生的可能性,最终得到可用来预测云南地区及邻区M≥6.0地震未来中短期发生可能性大小的综合概率P指标。计算分析表明:①使用综合概率P预测未来半年云南地区及邻区M≥6.0强震,能通过R值检验;②取P异常阀值为20%、30%,可对强震进行不同级别预警,P值小于20%为安全,P值为20%~30%为黄色预警,为云南地区及邻区未来存在发生M≥6.0地震的可能性;P值大于30%为红色预警,表示未来半年云南地区及邻区将发生M≥6.0地震。

女性中层管理者职业继续发展阻碍因素的探索研究

为定位“玻璃天花板”的最关键因素及特殊阶段呈现的重要因素,文章聚焦“分水岭”阶段的女性中层管理者群体,构建女性职业发展阻碍因素的理论模型。结果表明,影响女性中层管理者职业继续发展的因素及作用机制可以结合能力—动机—机会(Ability-Motivation-Opportunity,AMO)框架加以理解。组织结构同质化、结构性晋升阻碍和职场性别劣势限制了女性中层管理者职业继续发展机会;社会性别分工认同、工作-家庭平衡成功观,以及低职场性别效能削弱了女性中层管理者职业继续发展的动机;由于工作压力和社会性别评价压力,以及职场任务导向认知、工具性内隐领导原型和性别特质的影响,许多女性中层管理者呈现出侧重任务导向,但任务导向执行低效的领导行为特点,降低了女性中层管理者的领导力,进而制约了女性中层管理者的职业继续发展能力。

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%。

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%。

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。

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。