Neighbor sum distinguishing list total coloring of subcubic graphs

Let \(G=(V, E)\) be a simple graph and denote the set of edges incident to a vertex v by E(v). The neighbor sum distinguishing (NSD) total choice number of G, denoted by \(\mathrm{ch}_{\Sigma }^{t}(G)\), is the smallest integer k such that, after assigning each \(z\in V\cup E\) a set L(z) of k real numbers, G has a total coloring \(\phi \) satisfying \(\phi (z)\in L(z)\) for each \(z\in V\cup E\) and \(\sum _{z\in E(u)\cup \{u\}}\phi (z)\ne \sum _{z\in E(v)\cup \{v\}}\phi (z)\) for each \(uv\in E\). In this paper, we propose some reducible configurations of NSD list total coloring for general graphs by applying the Combinatorial Nullstellensatz. As an application, we present that \(\mathrm{ch}^{t}_{\Sigma }(G)\le \Delta (G)+3\) for every subcubic graph G.     

MSE performance of the weighted average estimators consisting of shrinkage estimators when each individual regression coefficient is estimated

In this paper, we analytically derive the exact formula for the mean squared error (MSE) of two weighted average (WA) estimators for each individual regression coefficient. Further, we execute numerical evaluations to investigate small sample properties of the WA estimators, and compare the MSE performance of the WA estimators with the other shrinkage estimators and the usual OLS estimator. Our numerical results show that (1) the WA estimators have smaller MSE than the other shrinkage estimators and the OLS estimator over a wide region of parameter space; (2) the range where the relative MSE of the WA estimator is smaller than that of the OLS estimator gets narrower as the number of explanatory variables k increases.     

Statistical considerations in clinical trial design with event-free survival as the primary efficacy endpoint

In late-phase confirmatory clinical trials in the oncology field, time-to-event (TTE) endpoints are commonly used as primary endpoints for establishing the efficacy of investigational therapies. Among these TTE endpoints, overall survival (OS) is always considered as the gold standard. However, OS data can take years to mature, and its use for measurement of efficacy can be confounded by the use of post-treatment rescue therapies or supportive care. Therefore, to accelerate the development process and better characterize the treatment effect of new investigational therapies, other TTE endpoints such as progression-free survival and event-free survival (EFS) are applied as primary efficacy endpoints in some confirmatory trials, either as a surrogate for OS or as a direct measure of clinical benefits. For evaluating novel treatments for acute myeloid leukemia, EFS has been gradually recognized as a direct measure of clinical benefits. However, the application of an EFS endpoint is still controversial mainly due to the debate surrounding definition of treatment failure (TF) events. In this article, we investigate the EFS endpoint with the most conservative definition for the timing of TF, which is Day 1 since randomization. Specifically, the corresponding non-proportional hazard pattern of the EFS endpoint is investigated with both analytical and numerical approaches.     

A Bayesian adaptive design for biosimilar trials with time-to-event endpoint

A biosimilar drug is a biological product that is highly similar to and at the same time has no clinically meaningful difference from licensed product in terms of safety, purity, and potency. Biosimilar study design is essential to demonstrate the equivalence between biosimilar drug and reference product. However, existing designs and assessment methods are primarily based on binary and continuous endpoints. We propose a Bayesian adaptive design for biosimilarity trials with time-to-event endpoint. The features of the proposed design are twofold. First, we employ the calibrated power prior to precisely borrow relevant information from historical data for the reference drug. Second, we propose a two-stage procedure using the Bayesian biosimilarity index (BBI) to allow early stop and improve the efficiency. Extensive simulations are conducted to demonstrate the operating characteristics of the proposed method in contrast with some naive method. Sensitivity analysis and extension with respect to the assumptions are presented.     

农民工工资支付法治化问题研究——基于对《保障农民工工资支付条例》的解读

《保障农民工工资支付条例》(以下简称《条例》)是我国当前面对农民工工资支付出现的新问题制定的一套具有规范化、权威性和有效性的法律保障机制。从农民工工资支付法治化保障机制的制度特点来看,《条例》的主体责任更加明确化,处罚方式强调规范化,监管过程具备严密化,监管主体呈现多元化。《条例》采取全程协同监管手段消除“灰色治理”的“梗阻”,用“法律红线”落实并明确各方的法律主体责任,以细化规定的方式打通工资支付的“绿色通道”,从而能够保障《条例》的有效落实。为了更好地保障农民工的切身利益,《条例》主要从实践逻辑层面上落实法治化保障措施:明确不同监管主体在监督检查领域中的职能定位,对用人单位实行严格的资格认定与法律责任制,在监管领域中始终关注劳有所得的民生问题。     

网络社区发现算法在流动表建模中的设计与应用

代际流动表可以统计子代与其父代社会地位配对数据的交互频数，反映了社会资源占有的优劣势在父子两代人之间的比较。对财富、阶级、特权等社会基本特征演变的实证考察，均依赖于代际流动表的量化分析。对数线性模型是流动表建模分析的基本工具，通过对列联表单元格频数进行拟合，可以识别流动表行分类与列分类之间的强弱交互效应，刻画父子社会地位间的交互结构。本文利用复杂网络社区发现算法分析父子社会地位的关联结构，针对简约对数线性模型拟合精度不够的问题，提出一种新的建模思路：利用社区发现算法对简约对数线性模型的残差列联表进行关联关系挖掘，将发现的社区效应作为附加参数约束引入原对数线性模型，以改善数据的拟合情况。由于该方法只在原简约对数线性模型中增加了一个参数约束，因此仍可以保证建模结果的简洁性及理论意义，同时社区效应补充了原对数线性模型对经验数据结构的解读。论文用此方法对来源于中国综合社会调查数据的经验代际职业流动表进行建模分析，较好地解释了子代职业阶层与父代职业阶层间的关联模式。     

基于超总体模型的设计效应分解

本文基于超总体模型研究抽样调查中设计效应的计算问题。首先以随机效应模型为基础，明确了简单随机、二阶段、不等概率和分层抽样对应的超总体模型，进而通过所给模型推导出分层、类集、加权单因素设计效应的计算公式和多因素组合的设计效应计算公式并给出了对应估计量，公式表明：多因素同时存在的组合设计效应等于对应单因素设计效应的乘积。最后，对设计效应的理论值、估计值和真实值之间的关系进行了蒙特卡洛仿真，并利用相对偏倚、相对均方误进行了评价。本文的研究，对复杂抽样设计中正确计算、使用设计效应具有指导意义。     

考虑实时预倒箱的出口箱堆场多场桥调度优化

在出口集装箱堆场的实际作业过程中，倒箱是制约场桥作业效率的瓶颈之一。为提高出口箱堆场的作业效能，减少船舶装船作业时间，采用实时预倒来降低倒箱的影响，研究出口箱堆场多场桥调度优化问题。考虑待提箱作业次序固定、场桥间保持安全距离及不可跨越的现实约束，兼顾内集卡的等待上限，侧重场桥作业过程中的实时预倒箱，构建了以带惩罚因子的内集卡总等待时间最少为目标的混合整数线性规划模型。基于问题自身的特点设计了混合和声模拟退火算法，得出了各场桥的行走路径与实时预倒箱方案。在算例实验中，通过与不考虑实时预倒箱的方案、FCFS方案以及下界进行对比，验证了考虑实时预倒箱的场桥调度模型及算法的有效性，为集装箱码头出口箱堆场的场桥调度提供参考。     

Online MapReduce processing on two identical parallel machines

In this work we investigate the online over-list MapReduce processing problem on two identical parallel machines, aiming at minimizing the makespan. Jobs are revealed one by one, and each job consists of one map task and one reduce task. The map task can be arbitrarily split and processed on both machines simultaneously, while the reduce task has to be processed on a single machine and it cannot be started unless the map task has been completed. We first show that the general case of the problem reduces to the classical two machine online scheduling model with an optimal competitive ratio of 3/2. For a special case where the map task is at least as long as the reduce task, we prove that no online algorithm can be less than 4/3-competitive. An optimal Greedy algorithm with a matching competitive ratio is proposed as well.