Scheduling problems on tardiness penalty and earliness award with simply linear processing time

In this paper, a single-machine scheduling model with a given common due date and simple linear processing times was considered. The objective is the total weighted tardiness penalty and earliness award. Some polynomial time solvable cases for this problem are given. A dynamic programming algorithm was provided and a branch and bound algorithm for general case of the problem was provided based on a rapid method for estimating the lower bound.

A dynamic programming algorithm for scheduling problems on earliness award and tardiness penalty with time-dependent processing time

In this paper, a single-machine scheduling model with a given common due date is considered. Job processing time is a linear decreasing function of its starting time. The objective function is to minimize the total weighted earliness award and tardiness penalty. Our aim is to find an optimal schedule so as to minimize the objective function. As the problem is NP-hard, some properties and polynomial time solvable cases of this problem are given. A dynamic programming algorithm for the general case of the problem is provided.

Risk scores for allograft failure: Are they still useful in liver recipients from donation after circulatory death?

BACKGROUND Liver grafts from donation after circulatory death(DCD)are associated with a higher risk of early graft dysfunction,determined by the warm ischemia and cold ischemia times.It is essential to have precise criteria to identify this complication in order to guide therapeutic strategies.AIM To validate different graft and recipient survival scores in patients undergoing liver transplantation(LT)with DCD grafts.METHODS A retrospective and observational unicentric study was conducted on 65 LT patients with grafts obtained from controlled DCD donors from November 2013 to November 2022.The United Kingdom(UK)risk score,early allograft dysfunction(EAD)Olthoff score,and model for early allograft function(MEAF)score were used to evaluate the risk of graft and recipient survival post-transplant.For survival analysis purposes,we used the Kaplan-Meier method,and the differences between subgroups were compared using the log-rank(Mantel-Cox)test.RESULTS Sixty-five patients were included in the study.The UK risk score did not demonstrate predictive capacity for recipient or graft survival.However,in donors aged over 70 years old(18.4%),it significantly predicted graft survival(P<0.05).According to Kaplan-Meier survival curves,graft survival rates at 6 months,2 years,and 5 years in the futility group dramatically decreased to 50%compared to the other groups(log-rank 8.806,P<0.05).The EAD Olthoff and MEAF scores did not demonstrate predictive capacity for recipient or graft survival.Based on Kaplan-Meier survival curves,patients with a MEAF score≥7 had a lower graft survival rate at 6 months,2 years,and 5 years compared to patients with a lower MEAF score(log-rank 4.667,P<0.05).CONCLUSION In our series,both UK DCD risk score and MEAF score showed predictive capability for graft survival.

Ecological risk assessment and early warning of heavy metal cumulation in the soils near the Luanchuan molybdenum polymetallic mine concentration area,Henan Province,central China

The Luanchuan molybdenum polymetallic mine concentration area is rich in mineral resources and has a long history of mining.The environmental impact of long-term mining activities cannot be ignored.It is of great significance to study the ecological risk and the accumulation trends of heavy metals in the soil of mining areas for scientific prevention and control of heavy metal pollution.Taking the Taowanbeigou River Basin in the mine concentration area as the research object,the ecological pollution risk and cumulative effect of heavy metals in the soil of the basin were studied by using the comprehensive pollution index method,potential ecological risk assessment method and geoaccumulation index method.On this basis,the cumulative exceeding years of specific heavy metals were predicted by using the early warning model.The comprehensive potential ecological risk of heavy metals in the soil near the Luanchuan mine concentration area is moderate,and the single element Cd is the main ecological risk factor,with a contribution rate of 53.6%.The overall cumulative degrees of Cu and Pb in the soil are“none-moderate”,Zn and Cd are moderate,Mo has reached an extremely strong cumulative level,Hg,As and Cr risks are not obvious,and the overall cumulative risks order is Mo>Cd>Zn>Cu>Pb>Hg.According to the current accumulation rate and taking the risk screening values for soil contamination of agricultural land as the reference standard,the locations over standard rates of Cu,Zn and Cd will exceed 78%in 90years,and the over standard rate of Pb will reach approximately 57%in 200 years.The cumulative exceeding standard periods of As,Cr and Hg are generally long,which basically indicates that these elements do not pose a significant potential threat to the ecological environment.Mining activities will accelerate the accumulation of heavy metals in soil.With the continuous development of mining activities,the potential pollution risk of heavy metals in the soil of mining areas will also increase.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Nomogram established using risk factors of early gastric cancer for predicting the lymph node metastasis

BACKGROUND For the prognosis of patients with early gastric cancer(EGC),lymph node metastasis(LNM)plays a crucial role.A thorough and precise evaluation of the patient for LNM is now required.AIM To determine the factors influencing LNM and to construct a prediction model of LNM for EGC patients.METHODS Clinical information and pathology data of 2217 EGC patients downloaded from the Surveillance,Epidemiology,and End Results database were collected and analyzed.Based on a 7:3 ratio,1550 people were categorized into training sets and 667 people were assigned to testing sets,randomly.Based on the factors influencing LNM determined by the training sets,the nomogram was drawn and verified.RESULTS Based on multivariate analysis,age at diagnosis,histology type,grade,T-stage,and size were risk factors of LNM for EGC.Besides,nomogram was drawn to predict the risk of LNM for EGC patients.Among the categorical variables,the effect of grade(well,moderate,and poor)was the most significant prognosis factor.For training sets and testing sets,respectively,area under the receiver-operating characteristic curve of nomograms were 0.751[95%confidence interval(CI):0.721-0.782]and 0.786(95%CI:0.742-0.830).In addition,the calibration curves showed that the prediction model of LNM had good consistency.CONCLUSION Age at diagnosis,histology type,grade,T-stage,and tumor size were independent variables for LNM in EGC.Based on the above risk factors,prediction model may offer some guiding implications for the choice of subsequent therapeutic approaches for EGC.

Pattern changes and early risk warning of Spartina alterniflora invasion:a study of mangrove-dominated wetlands in northeastern Fujian,China

The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Water risk early warning systems based on the water environmental carrying capacity(WECC)are powerful and effective tools to guarantee the sustainability of rivers.Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload.In addition,existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators.Given the periodicity of the socioeconomic system,however,the water environmental system also follows a trend of cyclical fluctuations.Thus,it is meaningful to monitor and use this periodicity for the early warning of the WECC.In this study,we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework.We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system.We selected the North Canal Basin(an essential connection among the Beijing-Tianjin-Hebei region)in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018–2023.We found that the analysis of the prosperity index was helpful in predicting the WECC,to some extent.The result demonstrated that the early warning system provided reliable prediction(root mean square error of 0.0651 and mean absolute error of 0.1418),and the calculation results of the comprehensive early warning index(CEWI)conformed to the actual situation and related research in the river basin.From 2008 to 2023,the WECC of most water environmental management units in the basin had improved but with some spatial differences:the CEWI was generally poor in areas with many human disturbances,while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume.Finally,through a sensitivity analysis of the indicators,we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin.Overall,the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.This framework could also assist in implementing corresponding management measures in advance,especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.

联合国全民早期预警目标下中国气候风险管理前景分析

全球气候变暖对自然生态系统和人类社会产生了广泛而深远的影响,特别是加剧了气候变化风险,未来这种风险将更加复杂且难以管理。早期预警是减轻灾害风险和适应气候变化的重要手段。文章系统回顾了国内外早期预警发展及早期预警技术的全球演变历程,归纳总结了国际早期预警从概念提出到多灾种早期预警系统发展的4个阶段,剖析了气候变化背景下联合国全民早期预警倡议的3方面内涵。结合我国早期预警服务的现状和发展模式,以及新形势下面临的气候风险、预警需求和技术差距,提出了在联合国全民早期预警目标下,我国在气候风险管理方面的3个优先方向。一是加强对复合型极端天气气候事件发生发展机理和预报预警的科学研究,以及人类社会经济系统中潜在临界要素的研究,评估极端事件和临界要素引爆后的潜在级联影响和规模,提高灾害风险认知能力,拓深、拓广已有多灾种早期预警系统,提升防范新型气候灾害风险能力;二是识别气候变化对行业、领域和区域绿色低碳转型可能造成的产业风险,加快构建面向气候和气候变化尺度的国家级气候安全早期预警平台,提升全社会应对气候风险的能力;三是开展多灾种早期预警国际合作,帮助尚无早期预警系统或早期预警系统效力不足的国家建立和完善早期预警系统,重点是厘清区域和国家存在的主要气候灾害风险和等级,以及适应气候变化的紧迫需求,提供多品类气象防灾减灾公共产品,提高国际影响力。

早期预警评分联合SBAR沟通模式在高危新生儿中的应用

目的探讨改良早期预警评分(modified early warning score,MEWS)联合SBAR[现状(situation)、背景(background)、评估(assessment)、建议(recommendation)]沟通模式在高危新生儿中的应用效果,为临床提供一种有效评估患儿病情变化的沟通方法。方法采用前-后对照研究方法,选取2022年8月至9月入住本院新生儿科病房的高危新生儿270例作为研究对象。以8月入院的高危新生儿为对照组,9月入院的高危新生儿作为试验组,每组分别纳入135例患儿。对照组患儿按照常规护理实施病情观察,试验组患儿在对照组基础采用MEWS联合SBAR沟通模式实施病情观察。比较两组高危新生儿预警事件发生情况,护士预警事件与医生处理事件的一致率,医生对护士工作的满意率。结果两组均完成研究。对照组中63.6%的预警事件是由护士发现,试验组中92.6%的预警事件是由护士发现,两组比较,差异具有统计学意义(χ2=16.622,P<0.001)。试验组护士预警事件与医生处理事件的一致性(Kappa系数=0.926)高于对照组(Kappa系数=0.641);试验组医生对护士在专科知识、抢救应急能力、掌握病情情况、及时观察病情变化、医护配合、工作积极性、沟通能力、心理素质方面的满意率均高于对照组(80.0%~95.0%v30.0%~55.0%),两组比较,差异具有统计学意义(均P<0.05)。结论MEWS与SBAR沟通模式联合应用,有助于护士准确评估患儿病情变化,及时有效完成医护沟通,提高护士观察、沟通和处理能力,也提高了医生对护士工作的满意度。

郑州市肺癌早期筛查患病风险横断面研究

目的分析参加肺癌早期筛查者风险及相关影响因素,并根据生存情况研究预后因素,以期最终为肺癌防治提供基线数据。方法采取整群抽样的方法,选取2020年郑州市10区6县40~75岁郑州市户籍居民为筛查对象,通过自愿参加并填写评估问卷,评估出肺癌高危人群,再对高危人群行复筛三项(肿瘤标志物、低剂量螺旋CT及肺功能)检查。最后采取主动加被动随访方式收集确诊肺癌患者信息。对筛查的数据进行统计描述,表述不同特征人群流行病学结果;采用多因素logistic回归方法进行统计分析,比较不同因素各项结果差异。结果2020年郑州市肺癌早期筛查完成评估50128例,完成率100.26%,调查平均年龄(59.86±17.67)岁,男女比例为0.81∶1。其中高危检出率为30.15%。多因素logistic回归分析显示:男性(吸烟)(OR=5.43,95%CI:5.20~5.67)、有烟草接触史(OR=3.82,95%CI:3.67~3.98)、一级亲属曾患肺癌(OR=12.06,95%CI:11.02~13.20)等人群更易患肺癌(均P<0.05)。结论男性(吸烟)、接触二手烟、一级亲属患癌、曾经确诊其他肿瘤、肺部感染症状、“日常生活中出现胸闷,气促,呼吸困难”、“近3年内曾经受较大的精神创伤”因素是肺癌高危的独立危险因素,应重点关注并加以有效干预措施。

基于风险评估模型的传染病预警体系的建立

目的建立一套基于风险评估模型的传染病预警体系,推动传染病管理水平的提升。方法通过搜集、整理最新的研究成果,参考相关的传染病预警评价指标体系的理论框架,构建基于风险评估模型的传染病预警体系。结果本次研究共纳入了12名专家,筛选出了三个一级指标,涉及流行前期指标、典型症状期指标、非典型症状期指标等,同时涉及7个二级指标。体系的必要性平均得分为8.27±0.24分,可获得性平均得分为7.74±0.34分。结论基于风险评估模型的传染病预警体系的建立有利于当地及时掌握传染病流行趋势,并进行调查指标分布趋势分析,然后提出相应的预警方案,可为有效预防传染病提供相关的依据。

福建省贝类毒素风险预警与管控的现状与建议

贝类毒素主要由有毒赤潮藻产生,在海洋环境中分布广泛,它可通过食物链传递,对民众生命健康造成严重威胁。福建省近岸海域是我国赤潮高发海域之一,近年来在漳州、泉州等地曾发生民众食用贝类中毒事件。为保障民众健康和生命安全,维护海洋渔业经济健康发展,本文对福建省贝类毒素风险监测与管控、赤潮灾害预警与应急处置预案等相关政策、措施进行总结,结合近年来福建省海水贝类贝毒污染状况,对存在的主要问题进行分析,并针对性地提出健全相关法律法规、制定贝毒风险预警与应急处置预案、完善贝类毒素监测方案、严格产毒藻预警浓度、建立贝类毒素分级管控制度等对策和建议,以期为赤潮减灾、水产品质量安全管理,以及贝类毒素风险预警与管控体系的完善提供参考。

基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

地表形变是一种严重的地质灾害现象,不仅严重影响灾害区居民的日常生活,而且会造成巨大的社会经济危害,尤其在采煤区。针对传统地表沉陷监测方法费时费力、无法获取地表沉降面状信息、难以进行地表沉陷灾害评估的不足,基于高分辨率SAR卫星影像,利用永久散射体合成孔径雷达干涉测量(PS-InSAR)技术对山西省晋城市晋城矿区2018年1月至2018年12月期间地表沉陷进行监测,分析获取了该地区地表连续形变情况,并利用该技术获取的海量PS点建立支持向量机(SVM)地质灾害风险评估预警模型,对晋城矿区周边居民点地质灾害风险进行了识别和预测。结果表明:晋城矿区10个煤矿及其周边区域存在较大的地表形变;晋城矿区平均LOS向年平均地表形变速率范围为-37~30.3 mm/a;PS-InSAR技术在晋城矿区地表形变监测中具有可行性,且可以实现矿区地质灾害风险综合识别和预警。

冬小麦农业气象灾害风险预警的业务实践与思考

开展冬小麦农业气象灾害风险预警工作是当前保障小麦生产安全和防灾减损的重要工作之一。2022年开始气象部门和农业部门联合开展了冬小麦农业气象灾害风险预警的业务服务工作,取得了一些技术成果和成功案例,但也存在一些问题和不足。本文简述了农业气象灾害风险预警业务的内涵,提出了综合农业气象灾害可能性预报、危险性和脆弱性预估,构建基于风险矩阵的冬小麦农业气象灾害风险预警业务的技术框架,并以2022年小麦干热风风险预警工作为例,展示了冬小麦农业气象灾害风险预警工作的技术流程。冬小麦农业气象灾害风险预警工作刚刚起步,尚有很多工作不完善,包括如何与已有的灾害风险评估成果进行深入融合,开展定量化的灾害影响预报和预警指标率定等。此外,在提升冬小麦农业气象灾害风险预警的准确率和开展服务效益评估等方面,未来还需要做大量的研发工作。

基于概率模型的四川省降水诱发地质灾害风险预警方法研究

开展降水诱发地质灾害风险预警是降低地质灾害风险的重要手段之一。基于2008-2019年四川省地质灾害历史灾情数据,在地理分区基础上,以地形、岩土、植被、气候因子为区划评价因子,采用信息量模型,将四川省划分为7个地质灾害分区。以正态化处理后的地质灾害有效降水量为建模因子,采用高斯拟合方法,建立各分区降水诱发地质灾害概率模型,并进行地质灾害预警检验。结果表明:(1)四川省可划分为盆地及盆周山区低易发区、中等易发区、高易发区,西南山地次易发区、易发区,川西高原次易发区、易发区等7个地质灾害分区。(2)ROC(Receiver Operating Characteristic)曲线分析表明,盆地及盆周山区的地质灾害分区精度略低于西南山地区和川西高原区的分区精度。(3)各分区正态化处理后的有效降水量与地质灾害频率具有很好的高斯分布关系。(4)建立的降水诱发地质灾害概率模型的地质灾害预警成功率为72%,当日降水量≥10 mm时,地质灾害预警成功率为83%。

幕上高血压性脑出血微创颅内血肿抽吸引流术后早期神经功能恶化危险因素分析

目的分析幕上高血压性脑出血微创颅内血肿抽吸引流术后发生早期神经功能恶化(early neurological deterioration,END)的危险因素。方法本研究为回顾性病例研究,连续纳入2018年10月—2022年12月首都医科大学附属北京天坛医院急诊神经内科收治的微创颅内血肿抽吸引流术治疗的幕上高血压性脑出血患者,记录患者人口学特征和临床信息,以及血肿体积、部位、周围灌注等影像学信息。END定义为手术后24 h内NIHSS评分增加≥4分或GCS评分下降≥2分。将单因素分析中差异有统计学意义的变量纳入多因素logistic回归模型(后退法),分析影响患者END的独立危险因素。绘制独立危险因素预测END的ROC曲线并计算AUC,采取De Long检验比较不同独立危险因素的预测能力。结果共入组157例患者,平均年龄为(57.1±13.1)岁。20例(12.7%)患者出现END。多因素logistic回归分析显示,术前血肿体积(OR 1.024,95%CI 1.001~1.047,P=0.043)、术后血肿扩大(OR 41.605,95%CI 7.405~233.765,P<0.001)、术前低灌注体积(OR 1.011,95%CI 1.002~1.020,P=0.012)等3个因素可独立预测END的发生。其中术前低灌注体积ROC的AUC为0.921,敏感度为0.824,特异度为0.891,截断值为119.0 mL。De Long检验显示,术前低灌注体积对END的预测效力优于术前血肿体积及术后血肿扩大。结论术前低灌注体积与幕上高血压性脑出血患者微创颅内血肿抽吸引流术后发生END独立相关,低灌注体积越大,术后END发生的风险越高。

我国经济风险的实时监测:结构性拐点判别与预警信息辨识

实时监测经济风险、判别经济风险演化方向,对于促进高质量发展和高水平安全良性互动具有重要意义。本文运用马尔科夫区制转移偏正态分布模型甄别经济风险的结构变迁路径,并基于驱动结构分解、概率分布矩、概率密度函数以及尾部风险等多维视阈,有效辨析经济和金融状况对于经济风险的预警功能。研究发现,首先,随着经济增长概率分布的风险区制由低到高,经济增长增速预期降低、波动区间扩张以及下行风险升高。其次,经济风险在2012年存在显著的结构性拐点,在突变点后,偏度系数由正转负,经济步入长期的下行风险通道。此外,经济风险在2019年以及2021年前后存在阶段性拐点,短期内经济上行概率较高。然后,经济风险的动态演化路径表现出显著的“事件驱动”特征,在极端事件冲击下,经济下行风险有所增加。最后,经济和金融状况对于经济风险的预警功能存在明显的异质性,其中经济状况无法提供经济风险的预警信息,而金融状况能够灵敏地识别与捕捉经济增长的风险信息,且在经济高风险时期,金融状况的预警功能更为显著。

急性胸痛患者早期风险评估研究进展

急性胸痛是急诊常见的症状之一,早期精准识别高风险性和低风险性胸痛有益于高危患者得到及时诊疗以及低危患者减少过度医疗,同时改善预后和缓解院前急救及急诊科医疗资源的严峻。近年来急性胸痛患者早期危险分层工具不断开发,其敏感度和特异度各有不同。本文对目前急性胸痛患者早期风险评估的研究进展进行综述。

大数据背景下的电子商务信用风险预警研究

为了提高电子商务信用风险预警效果,设计了一种大数据背景下的电子商务信用风险预警方法。首先设计电子商务信用风险预警指标,并采集大量的电子商务信用风险数据,然后分别采用最小二乘支持向量机和神经网络对电子商务信用风险预警进行建模,并对它们预警结果进行组合,最后与传统方法进行电子商务信用风险预警对比分析,分析结果表明设计的方法可以准确找到电子商务信用风险预警变化特点,电子商务信用风险预警正确率超过94%,远远高于传统方法的电子商务信用风险预警结果,大幅度降低了电子商务信用风险的误警率,具有十分明显的优越性。