Media planning by optimizing contact frequencies

In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners.optimization;contact frequency;effective reach;media planning

Media planning by optimizing contact frequencies

In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners

Capacity planning of prisons in the Netherlands

In this paper we describe a decision support system developed to help in assessing the need for various type of prison cells. In particular we predict the probability that a criminal has to be sent home because of a shortage of cells. The problem is modelled through a queueing network with blocking after service. We focus in particular on the new analytical method to solvethis network.decision support system;capacity planning;prison cells

Capacity planning of prisons in the Netherlands

In this paper we describe a decision support system developed to help in assessing the need for various type of prison cells. In particular we predict the probability that a criminal has to be sent home because of a shortage of cells. The problem is modelled through a queueing network with blocking after service. We focus in particular on the new analytical method to solve this network

Анализ отдаленных результатов санаторно-курортного лечения детей с реактивными артритами

На підставі вивчення віддалених результатів санаторно-курортного лікування з використанням клініко-лабораторних і функціональних параметрів 50 дітей з реактивними артритами встановлений сприятливий ефект в перебігу захворювання, що виявлялося відсутністю загострень, поліпшенням клінічної картини захворювання, зменшенням проявів суглобового синдрому, підвищенням функціональної активності нервово-м'язового апарату, поліпшенням показників якості життя.On the basis of study of remote results of resort medical treatment with the use of clinics,-laboratory and functional at 50 children with reactive arthritics a favorable effect in the flow of disease is set, that showed up absence of intensifications, improvement of clinical picture of disease, reduction of displays of joint syndrome, rise of functional activity of muscle vehicle, by the improvement of indexes of quality of life

An automated approach to identify scientific publications reporting pharmacokinetic parameters [version 1; peer review: awaiting peer review]

Pharmacokinetic (PK) predictions of new chemical entities are aided by prior knowledge from other compounds. The development of robust algorithms that improve preclinical and clinical phases of drug development remains constrained by the need to search, curate and standardise PK information across the constantly-growing scientific literature. The lack of centralised, up-to-date and comprehensive repositories of PK data represents a significant limitation in the drug development pipeline.In this work, we propose a machine learning approach to automatically identify and characterise scientific publications reporting PK parameters from in vivo data, providing a centralised repository of PK literature. A dataset of 4,792 PubMed publications was labelled by field experts depending on whether in vivo PK parameters were estimated in the study. Different classification pipelines were compared using a bootstrap approach and the best-performing architecture was used to develop a comprehensive and automatically-updated repository of PK publications. The best-performing architecture encoded documents using unigram features and mean pooling of BioBERT embeddings obtaining an F1 score of 83.8% on the test set. The pipeline retrieved over 121K PubMed publications in which in vivo PK parameters were estimated and it was scheduled to perform weekly updates on newly published articles. All the relevant documents were released through a publicly available web interface (https://app.pkpdai.com) and characterised by the drugs, species and conditions mentioned in the abstract, to facilitate the subsequent search of relevant PK data. This automated, open-access repository can be used to accelerate the search and comparison of PK results, curate ADME datasets, and facilitate subsequent text mining tasks in the PK domain.</ns4:p