Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we develop a mixed integer linear optimization formulation (MIP) to minimize the risk of failing response time targets. Given the stochasticity of the environment in terms of incident numbers, location, timing, and duration, we use Sample Average Approximation (SAA) to find a robust deployment plan. To overcome the sparsity of real data, samples are provided by an incident generator that learns the spatio-temporal distribution and demand parameters of incidents from a real world historical dataset and generates sets of training incidents accordingly. To improve runtime performance across multiple samples, we implement a heuristic based on Iterated Local Search (ILS), as the solution is intended to create deployment plans quickly on a daily basis. Experimental results demonstrate that ILS performs well against the integer model while offering substantial gains in execution time.
Set at the interface between second language acquisition and second language writing, this study examines how different types of written corrective feedback (WCF) influence the writing accuracy of Chinese college students learning English as a Foreign Language (EFL) and whether WCF facilitates the students’ grasp of the focused linguistic knowledge. The participants (n = 60) were divided into direct correction group (n = 20), indirect error-coding group (n = 20) and metalinguistic explanation group (n = 20). The three groups wrote four essays in two months and received WCF for the first three essays on the five targeted error types, namely tense errors, confusion of different forms of a word, word (articles, prepositions, etc.) missing, errors in subject-verb agreement and inappropriate verb-noun collocations. The results show that all three types of WCF improved students’ writing accuracy but none of them had any statistical advantage, and the metalinguistic WCF was more effective than the other two forms in facilitating the acquisition of the targeted linguistic features. These results shed some light on teaching and consolidation of language points through writing in EFL contexts.
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