Croatia's bitter harvest: Total National Defence's role in the Croatian War of Independence

Croatia's successful bid for independence was one of the defining moments of the immediate post-Cold War period. Nevertheless, the means that Croatia used to obtain independence remains relatively unexamined by academics. This article focuses on the early period of Croatia's bid for secession, and specifically the role that Yugoslavia's policy of Total National Defence played in facilitating its independence. Unlike in Slovenia, where the legacies of Total National Defence facilitated Slovenia's bid for secession, in Croatia the decision of its political leaders meant that the country largely neglected its positive legacies, resulting in a protracted four-year struggle.     

面向混部云失败批处理作业的预测算法

为了降低混部云失败批处理作业的风险,使用K-means聚类算法将批处理作业分为四类,在分类的基础上提出了二层嵌套分类模型(two-layer nested classification model, TLNM),实现了基于TLNM的预测算法。基于Ali Trace 2018数据集上的实验结果表明,该算法的接受者操作特性(receiver operating characteristic, ROC)曲线明显优于其他常用分类器,ROC曲线下面积(即AUC)可以达到0.978,表明该算法具有良好的分类性能。同时召回率可以达到0.951,通过混淆矩阵可以看出TLNM算法能够准确预测出执行失败的批处理作业。     

The rise of targeted killing

Targeted killings have become a central tactic in the United States' campaigns against militant and terrorist groups in the Middle East, Asia, and Africa. Both ‘demand’ and ‘supply’ factors explain the rise of targeted killings. Demand for targeted killings increased as the United States faced new threats from militant groups that could not be effectively countered with conventional military force. Concerns about the political consequences of long-term military involvement overseas and American casualties led political leaders to supply more targeted killings. The conclusion discusses how this tactic may have unintended consequences as other states follow the United States use of targeted killings.     

Target selection in Lanchester combat: Linear-law attrition process

We develop the solution to a simple problem of target selection in Lanchester combat against two enemy force types each of which undergoes a “linear-law” attrition process. In addition to the Pontryagin maximum principle, the theory of singular extremals is required to solve this problem. Our major contribution is to show how to synthesize the optimal target selection policies from the basic optimality conditions. This solution synthesis methodology is applicable to more general dynamic (tactical) allocation problems. For constant attrition-rate coefficients we show that whether or not changes can occur in target priorities depends solely on how survivors are valued and is independent of the type of attrition process.