基于JIT思想对汽车玻璃生产线WIP的控制策略

本文分析了汽车玻璃加工生产线的生产流程,生产车间不同种类的在制品(WIP)数量很庞大,为此研究生产线的瓶颈工序,得到瓶颈工序生产线的切换时间对WIP库存的影响,研究生产线的切换时间、工序之间的生产节拍比和WIP库存数三者之间的关系,结合JIT和WIP库存控制策略来保证生产线的流畅生产,归纳出关键的工序节拍比下所需的WIP库存数的动态变化情况,从而动态地控制WIP库存的变化并最大程度地降低车间的WIP库存。     

浅论三十年代前后左翼叙事文学的颓废情调

20世纪二三十年代,左翼文学在突出革命主题的同时,不可避免地带有颓废情调。一方面表现在内容上的颓废叙述,一方面表现在选题上的颓废倾向。     

当代大学生信息素质教育

信息素质教育是大学生素质教育的重要组成部分.高校图书馆应开设有关课程,开展一系列教育活动,加强大学生信息能力的培养,大力发展信息专业教育.     

夏季车内存放时间对开封后瓶装饮用矿泉水中亚硝酸盐含量及菌落总数的影响

目的:探究夏季汽车内瓶装饮用矿泉水开封后的存放时间对水中亚硝酸盐含量及菌落总数的影响.方法:夏季将开封后的瓶装饮用矿泉水放置于汽车内,在第1 d、第3 d、第7 d和第14 d分别测定水中的NO2-含量及菌落总数.检验标准参考《食品安全国家标准饮用天然矿泉水检验方法》(GB 8538—2016)和《生活饮用水标准检验方法微生物指标》(GB/T 5750.12—2006).结果:开封后的瓶装饮用矿泉水在汽车内存放3 d后,菌落总数呈现轻微上升,其菌落总数平均值为7.5 CFU·mL-1、NO2-含量无明显变化;存放7 d后,其菌落总数、NO2-含量呈现急剧上升,其菌落总数平均值为95 CFU·mL-1,NO2-含量为0.125 mg·L-1;存放14 d后,其菌落总数、NO2-含量呈现大幅度上升,其菌落总数平均值为355 CFU·mL-1,NO2-含量为0.254 mg·L-1.结论:开封后的瓶装饮用矿泉水在夏季汽车内的存放时间为7 d时,其菌落数量仍符合《生活饮用水卫生标准》的限值(100 CFU·mL-1)要求,但接近临界值;NO2-含量不符合饮用天然矿泉水标准限值(0.005 mg·L-1),因此开封后的瓶装饮用矿泉水在夏季汽车内的存放时间接近或超过7 d时,不建议直接饮用.     

Rational Inattention in Uncertain Business Cycles

The paper proposes endogenous information choice as a channel through which uncertainty affects price dynamics. I consider a rational inattention model with volatility uncertainty and endogenous information processing capability. According to the model, firms' learning and optimal attention exhibits inertia and asymmetry in response to volatility changes. Firms choose to process more information when uncertainty rises, especially about aggregate conditions, and their pricing behavior changes accordingly. Using a Markov‐switching factor‐augmented vector autoregression (MS‐FAVAR), the paper also documents a significant positive correlation between volatility and firms' responsiveness to macro‐ and microlevel shocks, consistent with model predictions.     

The relationship between motivation,learning approaches,academic performance and time spent

Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the accounting students have a slightly higher score for deep learning compared to surface learning. Moreover, high intrinsic motivation and extrinsic motivation have a significant positive influence on deep learning. Next, deep learning leads to higher academic performance; surface learning on the other hand leads to lower academic performance. The effect of deep learning on performance still holds, when we control for time spent, gender and ability. Consequently we can conclude that a deep learning approach is much more than ‘simply’ spending a lot of time on studying.     

On dividends in the phase–type dual risk model

The dual risk model assumes that the surplus of a company decreases at a constant rate over time, and grows by means of upward jumps which occur at random times with random sizes. In the present work, we study the dual risk renewal model when the waiting times are phase-type distributed. Using the roots of the fundamental and the generalized Lundberg’s equations, we get expressions for the ruin probability and the Laplace transform of the time of ruin for an arbitrary single gain distribution. Then, we address the calculation of expected discounted future dividends particularly when the individual common gains follow a phase-type distribution. We further show that the optimal dividend barrier does not depend on the initial reserve. As far as the roots of the Lundberg equations and the time of ruin are concerned, we address the existing formulae in the corresponding Sparre-Andersen insurance risk model for the first hitting time, and we generalize them to cover also the situations where we have multiple roots. We do that working a new approach and technique, approach we also use for working the dividends, unlike others, it can be also applied for every situation.     

Estimating the Gerber–Shiu function by Fourier–Sinc series expansion

In this paper, we consider the nonparametric estimation of the Gerber–Shiu function in a compound Poisson risk model perturbed by diffusion. We present a more efficient estimator based on Fourier–Sinc series expansion. Our estimator is easily computed and has a faster convergence rate. Some simulation examples are provided to show that the estimator performs well when the sample size is finite.     

Iterated VaR or CTE measures: A false good idea?

The purpose of this paper is twofold. Firstly, we consider different risk measures in order to determine the solvency capital requirement of a pension fund. Secondly, we illustrate the impact of the time horizon of long-term guarantee products on these capital. We consider a financial market modelled by a common Black–Scholes–Merton model. We neglect the mortality and underwriting risks by assuming that the pension fund is fully hedged against these risks, which allows us to keep understandable and tractable formulæ (the longevity risk will be a part of future researches). A portfolio is built in this market according to different strategies and the pension fund offers a fixed guaranteed rate on a certain time horizon. We begin with well-known static risk measures (value at risk and conditional tail expectation measures) and then we consider their natural dynamic generalization. In order to be time consistent, we consider their iterated versions by a backward iterations scheme. Within the dynamic setting, we show that solvency capital can be expensive and that attention must be paid to the safety level considered.     

Time series momentum and moving average trading rules

We compare and contrast time series momentum (TSMOM) and moving average (MA) trading rules so as to better understand the sources of their profitability. These rules are closely related; however, there are important differences. TSMOM signals occur at points that coincide with a MA direction change, whereas MA buy (sell) signals only require price to move above (below) a MA. Our empirical results show MA rules frequently give earlier signals leading to meaningful return gains. Both rules perform best outside of large stock series which may explain the puzzle of their popularity with investors, yet lack of supportive evidence in academic studies.