Research on Unbounded Parallel-Batch Scheduling with Jobs Having Set-Up Times

We study the scheduling on an unbounded parallel-batch machine with jobs having set-up times to minimize a regular objective function which is either of the sumform or of the max-form.As we know,in the existing literature,the research about parallel-batch scheduling does not consider the set-up times of jobs.However,the set-up times of jobs are widely presented in practical applications.Each job considered in this paper has a set-up time.The set-up time of each batch is equal to the maximum set-up time of all jobs contained in the batch,and the processing time of each batch is equal to the longest processing time of the jobs contained in the batch.Depending on the different definitions of the completion time of a job,we consider two types of jobs:batch jobs and drop-line jobs.For batch jobs,the completion time of a job is given by the completion time of the batch containing this job.For drop-line jobs,the completion time of a job is given by the starting time of the batch containing this job plus the processing time of this job.In this paper,we give pseudo-polynomial time algorithms for the above scheduling problems in which the jobs have agreeable processing times and set-up times.In addition,when the objective functions are the total weighted completion time,the maximum lateness,and the maximum cost,we present a polynomial time algorithm,respectively.

The joint Laplace transforms for killed diffusion occupation times

The approach of Li and Zhou(2014)is adopted to find the Laplace transform of occupation time over interval(0,a)and joint occupation times over semi-infinite intervals(-∞,a)and(b,∞)for a time-homogeneous diffusion process up to an independent exponential time e_(q)for 0<a<b.The results are expressed in terms of solutions to the differential equations associated with the diffusion generator.Applying these results,we obtain explicit expressions on the Laplace transform of occupation time and joint occupation time for Brownian motion with drift.

A Comparison of Turnaround-Times for Two Popular Specimen Types Used for Newborn Toxicology: Meconium and Umbilical Cord Tissue

Background: Prenatal exposure to illicit substances is responsible for several long-term negative health consequences. It is critical for healthcare professionals to know the extent and scope of prenatal substance exposure in their cases. Several studies exist with mixed results comparing the effectiveness of umbilical cord tissue (UCT) and meconium (MEC) as toxicology specimen types. The specific aim of this study is to compare the use of UCT and MEC regarding the time interval between the birth of the neonate, receipt of the specimen at the laboratory, and the hospital’s receipt of the final toxicology report. Method: The study queried de-identified results of 5358 consecutive UCT and 706 MEC from our laboratory. Results: The mean time from birth to receipt of the specimen at the laboratory for MEC and UCT was 4.5 days ± 2.9 days and 2.8 days ± 1.9 days, respectively. The mean time from birth to final report for MEC was 6.9 days ± 3.8 days, 5.7 days ± 3.3 days, and 8.4 days ± 3.8 days for all MEC specimens, negative MEC, and positive MEC, respectively. The mean time from birth to final report for UCT was 4.3 days ± 2.4 days, 3.5 days ± 2.2 days, and 5.4 days ± 2.2 days for all UCT, negative UCT and positive UCT, respectively. Discussion/Conclusion: Receipt of drug test results of the neonate prior to release from the hospital is critical. This study shows that UCT offers an advantage when results are needed quickly to make informed decisions about the health and well-being of newborns.

Characterization of Hydroxyapatite Extracted from Crab Shell Using the Hydrothermal Method with Varying Holding Times

Hydroxyapatite(HA)is a bio ceramic commonly utilized in bone tissue engineering due to its bioactive and osteoconductive properties.Crab shells are usually disregarded as waste material despite their significant CaCO_(3) content,and have not been widely utilized in the synthesis of HA.This study aims to synthesize and analyze HA derived from crab shells using the hydrothermal method with different durations of holding time.This study utilized precipitated calcium carbonate(PCC)derived from crab shells.With a hydrothermal reactor set at 160℃ and varying holding times of 14(HA_14),16(HA_16),and 18(HA_18)h,a PCC and(NH4)2HPO4 mixture was used to synthesize HA.The synthesis results were analyzed using scanning electron microscopy(SEM),fourier transform infrared spectroscopy(FTIR),and X-ray diffraction(XRD)tests.This study has accomplished the synthesis of HA from crab shells.Nonetheless,the final product of synthesis still contained CaCO_(3) as an impurity.The prolonged hydrothermal holding time of 14 to 18 h resulted in a reduction of impurities while increasing the percentage of crystal weight and crystallite size of HA.Specimen CH_18 is the best-quality product generated in this study.This specimen produced HA with the highest percentage of crystal weight and crystallite size compared to the other specimens.Furthermore,specimen CH_18 exhibited the lowest concentration of impurities.The Ca/P ratio in this specimen was also the closest to 1.67.The Ca/P ratio,crystallite size,and crystal weight percentage of this specimen are 1.54,19.06 nm,and 99.1%,respectively.

Picturing the Times

A man with his face painted to appear like a spirit,participates in the He Neak Ta ritual in Phum Boeung village,northwest of Phnom Penh,Cambodia,on June 11.Cambodian villagers took part in a rare traditional guardian spirit ceremony praying for good fortune,rain,and prosperity as part of efforts to preserve this ancient tradition.

Trends of the Times

BUILDING a resilient workforce of the future means understanding your workers.Over the past five years,the Adecco Group has interviewed over 82,000 employees across 25 countries-including China-to capture global workers’perspectives and equip employers with the tools to future-proof their workforce.These are some of the findings about the labor market trends that have transformed the world of work since 2020。

Achieving Common Development in Step with the Times

As long as China and the EU adhere to strategic consensuses,keep in step with the times,and maintain mutual trust,they will be able to ensure the prosperity and stability of both sides.

Picturing the Times

Girls with flowers on their heads at a celebration for the traditional Ngarot festival in West Java,Indonesia,on December 13,2023.The festival is a traditional ceremony to welcome the rice planting season.

Picturing the Times

On September 25,locals and tourists pose for photos with a giant decorative flower basket on Tiananmen Square in Beijing to celebrate the forthcoming National Day.The four Chinese characters adorning the basket mean"blessing the motherland."(IC PHOTO)

Picturing the Times

Cherry blossoms at Qingchuan Pavilion in Wuhan,capital of central China’s Hubei Province,attract throngs of visitors on March 23.Illuminated by night lights,the cherry blossoms shone brightly,creating fantastic scenes of light and shadow with neon-clad bridges and landmark buildings along the Yangtze River.

Picturing the Times

Tourists sightsee on a boat in a massive lotus pond scenic area in Xinghua,Taizhou,east China’s Jiangsu Province,on August 5.When summer peaks,lotus flowers enter their blooming season,attracting throngs of tourists to admire their beauty.

Optimal Multi-Timescale Scheduling of Integrated Energy Systems with Hybrid Energy Storage System Based on Lyapunov Optimization

The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.

RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

基于TimeSVD++与DPC的推荐算法研究

针对使用奇异值分解(SVD)方法时需要填充矩阵的内容较多以及K-means算法受到K值的影响且数据集形状限制等问题,提出一种将TimeSVD++与改进的密度峰值聚类结合的方法。首先在SVD++的基础上引入参数时间因子,构建TimeSVD++模型;其次,采用将相似系数引入高斯核函数的方法,对密度峰聚类算法中的局部密度公式进行修正;引入信息熵确定最优截断距离,最后在数据集MovieLens-1M和MovieLens-100k上验证,并将实验结果与其它算法进行对比。结果表明:所提出的方法在MAE,RMSE,Recall和F1值指标上均优于其它的算法。

Times管理方案在心内科护士多参数心电监护仪警报疲劳中的应用

探讨Times管理方案在心内科护士多参数心电监护仪警报疲劳中的应用效果,以提高护士临床警报知识水平,改善警报管理态度和行为,减轻护士的警报疲劳,降低心电监护仪错误警报发生率。

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.