汉初封国经济发展及其原因探析

汉初封国经济的发展表现在两个方面 :在农业生产中 ,铁农具进一步发展 ,生产技术进步 ,作物品种增多 ,产量提高 ,农业人口增加 ,养殖业发展 ;在手工业方面 ,诸如冶铁、铸币、制盐、纺织等行业都取得了相当程度的发展。封国所处的优越地理环境和王国政府一系列政策措施的推行共同促成了封国经济的全面振兴。     

民国初年教育宗旨的理论基础

文章对蔡元培的教育方针思想作了回溯性的审视和评析 ,并给其以民初教育宗旨理论基础的准确复位 ,揭示了其与民初教育宗旨之间的内在联系 ,从而为民国教育宗旨的研究提供了一个新的视界     

A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

American options in discrete time can be priced by solving optimal stopping problems. This can be done by computing so-called continuation values, which we represent as regression functions defined recursively by using the continuation values of the next time step. We use Monte Carlo to generate data, and then we apply smoothing spline regression estimates to estimate the continuation values from these data. All parameters of the estimate are chosen data dependent. We present results concerning consistency and the estimates’ rate of convergence.     

近五年中英幼儿教师职前教育项目的比较及其启示

2014年以来，中英两国均出台了卓越取向的幼儿教师职前培养项目以提升幼儿教师质量，促进幼儿教育高质量发展。通过比较研究两国幼儿教师职前培养项目在培养机构、课程内容、学员情况、经费投入方面的特色及异同点，分析得出我国卓越幼儿园教师的培养实施可借鉴英国幼儿教师职前教育项目的经验，建立卓越幼儿园教师专业标准，开设职前职后一体化培养路径，健全培养质量监管与激励机制三方面的经验。     

大学生心理危机的识别与预警

心理危机的识别与预警既是预防心理危机发生的重要策略，也是对危机当事人和相关人进行生命教育和促进生命成长的重要机会。心理危机产生的原因具有复杂性，发生过程具有阶段性，危机现象具有特殊性，危机爆发具有极端性，危机发生也具有一定规律性。因此，在进行生命教育过程中，需要关注不同类别的生命个体、不同阶段的生命现象、处在规律中的独特生命个体，同时要注意识别生命危机信号和生命求救信号。     

On the Identification and Early Warning of College Students＇ Psychological Crisis from the Perspectiveof Life Education

心理危机的识别与预警既是预防心理危机发生的重要策略，也是对危机当事人和相关人进行生命教育和促进生命成长的重要机会。心理危机产生的原因具有复杂性，发生过程具有阶段性，危机现象具有特殊性，危机爆发具有极端性，危机发生也具有一定规律性。因此，在进行生命教育过程中，需要关注不同类别的生命个体、不同阶段的生命现象、处在规律中的独特生命个体，同时要注意识别生命危机信号和生命求救信号。     

A Preliminary Evaluation of the 1-2-3-Magic Parenting Program in an Australian Community Services Setting

Cognitive-behavioural parenting programs provide caregivers with alternative strategies to harsh disciplining practices but the choice of programs that are evidence-based in Australia is limited. Australian community service workers suggest that having a choice of evidence-based programs is important when matching programs to client needs, and that 1-2-3 Magic is a program used widely in Australia despite its small Australian evidence base. The efficacy of the 1-2-3 Magic program has previously been shown in a controlled trial in an Australian university research setting but little is known about its effectiveness in a “real-world” setting. This study examined a three-session group-format of the Australian 1-2-3 Magic & Emotion Coaching program in a typical metropolitan community service setting. Thirty-eight caregivers with children aged 2–6 years reported a significant decrease in disruptive child behaviour (ECBI), permissive parenting (PS), and parental depression and stress (DASS) from pre- to post-intervention, with results maintained at 3-month follow-up. While these findings suggest that a brief 1-2-3 Magic program is beneficial, it is important to note that methodological limitations (intervention-group only study design and possible confounding variables) do not allow ruling out alternative causes for these improvements. Further research will be needed to confirm that this program can generalise to “real-world” settings.     

Early stopping in seamless phase I/II clinical trials

In recent years, seamless phase I/II clinical trials have drawn much attention, as they consider both toxicity and efficacy endpoints in finding an optimal dose (OD). Engaging an appropriate number of patients in a trial is a challenging task. This paper attempts a dynamic stopping rule to save resources in phase I/II trials. That is, the stopping rule aims to save patients from unnecessary toxic or subtherapeutic doses. We allow a trial to stop early when widths of the confidence intervals for the dose-response parameters become narrower or when the sample size is equal to a predefined size, whichever comes first. The simulation study of dose-response scenarios in various settings demonstrates that the proposed stopping rule can engage an appropriate number of patients. Therefore, we suggest its use in clinical trials.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Moments of suprema of random variables

The supremum of random variables representing a sequence of rewards is of interest in establishing the existence of optimal stopping rules. Necessary and sufficient conditions are given for existence of moments of sup_n(X_n ? c_n) and sup_n(S_n ? c_n) where X₁, X₂, … are i.i.d. random variables, S_n = X₁ + … + X_n, and c_n = (nL(n))^1/r, 0 < r < 2, L = 1, L = log, and L = log log. Following Cohn (1974), “rates of convergence” results are used in the proof.