Reassessing Military Assistance to the Civil Powers: Are Traditional British Anti-Terrorist Responses Still Effective?

The provision of Military Assistance to the Civil Powers (MACP) encapsulates the traditional approach towards countering terrorism on the British mainland, which emphasises minimum force, proportionality, police primacy and the rule of law, with the military retained as a supporting instrument, being employed only as a measure of last resort. Recent terrorist incidents in major urban centres do, however, question the effectiveness of these established methods. This analysis initially codifies the conceptual issues that underscore the tensions between civil policing and military anti-terrorist approaches in the UK, and proceeds to evaluate and compare a number of operations that indicate the nature of the challenges facing the MACP apparatus. In particular, potential shortcomings in the MACP provisions are identified in the form of threat perception and deployment and enforcement gaps. The assessment finds that there are facets of MACP that are of enduring relevance in the current era, yet a continuing need persists to reconcile police and military imperatives toward serious civil disorder contingencies and conditions of emergency.     

信息技术与初中数学课程整合的实践

当前我国基础教育领域正在进行一场意义深远的课程改革,促进信息技术与课程整合成为其中的重要内容之一。信息技术与中小学数学学科的整合,使得中小学数学教学中学习内容和学习形式、教学观念和教学方式都产生了一系列的变化,本文通过在初中数学教学中进行的信息技术与课程整合实践尝试,总结了将信息技术有效地与中学数学课程整合,可以培养学生学习数学兴趣和实践能力。     

Scheduling with centralized and decentralized batching policies in concurrent open shops

This article considers batch scheduling with centralized and decentralized decisions. The context of our study is concurrent open shop scheduling where the jobs are to be processed on a set of independent dedicated machines, which process designated operations of the jobs in batches. The batching policy across the machines can be centralized or decentralized. We study such scheduling problems with the objectives of minimizing the maximum lateness, weighted number of tardy jobs, and total weighted completion time, when the job sequence is determined in advance. We present polynomial time dynamic programming algorithms for some cases of these problems and pseudo‐polynomial time algorithms for some problems that are NP‐hard in the ordinary sense. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 58: 17–27, 2011     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

A platform for initial testing of multiple camouflage patterns

The human visual system is still an important factor in military warfare; military personnel receive training on effective search strategies, and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy. Previous methods of camouflage assessment have, amongst others, used psychophysics to generate distinctiveness metrics. However, the population from which the human observers are drawn is often not well defined, or necessarily appropriate. In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task, and investigate whether trained military observers perform better than civilians. We use a two-alternative forced choice paradigm, with participants searching images of woodland for a replica military helmet displaying Olive Green, Multi Terrain Pattern, US Marine Pattern or, as a conspicuous control, UN Peacekeeper Blue. Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment. We conclude that when tasks involve very short stimulus presentation times, task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.     

ECONOMIC GROWTH AND VIOLENT INTERNATIONAL CONFLICT: 1875–1999

This paper extends the joint product model of military alliances to apply to the new strategic doctrine adopted by NATO in the 1990s. In particular, a choice must be made between protecting one's own territory and pooling forces for an alliancewide rapid reaction force. This new model accounts for a host of externalities and their implications for burden sharing, full financing, and allocative efficiency. The Pigouvian taxes that adjust for force thinning and attack deflection are shown to finance optimal border‐protecting forces under a variety of circumstances. Second‐best considerations arise owing to the pure publicness of rapid reaction forces. The ideal toll arrangement does not currently characterize NATO financing, nor is it likely to do so.