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Background: Many audits in primary care can be criticized because of the absence of verifiable data to measure outcomes, and the lack of a non‐participating group against which to compare results. Objective: Using Prescribing Analyses and Cost (PACT) data to quantify the effect of an audit in 15 practices. We sought to quantify the effect of the audit of benzodiazepine prescribing in a district by measuring the detailed changes in prescribing in participating practices before, during and after audit, and by comparing the volume of prescribing of these drugs in participating and neighbouring non‐participating practices. Methods: At the start of the audit, 291 993 patients in the Sefton district of North West England were registered with 55 general practices. Fifteen practices, caring for 87 902 patients, took part in an audit of benzodiazepine prescribing. We analysed routinely‐collected prescribing data to assess trends in benzodiazepine prescribing for those practices which took part in the audit and the remaining (non‐participatory) practices in the district. Main measures: The number of defined daily doses of benzodiazepine prescribed by those general practitioners auditing their prescribing of these drugs during the audit. The volume of benzodiazepines prescribed by all general practitioners in Sefton during the quarter immediately before and the quarter immediately after the audit. Results: There was a significant reduction in the number of defined daily doses dispensed for temazepam, nitrazepam, and lorazepam during the audit. There was a significantly greater reduction in the number of items prescribed by those doctors who took part in the audit than their colleagues who did not. Conclusions: An audit of benzodiazepine prescribing achieved a significant reduction in the volume of these drugs dispensed. An analysis of routinely‐collected data can usefully measure the result of an audit of prescribing.

Vernacular transformations of underused places give shape to Ephemeral Urban Dwellings (EUD). By reading the spatial patterns of use of three of these buildings, this paper demonstrates that EUD replicate the way activities and ideas of privacy are related to space in the previous and permanent homes left behind by its inhabitants. The case studies are located in central areas of Bogotá and, although ephemeral, they have stayed for years. Personal interviews and mental maps drawn by the interviewees as well as on site drawings and photography by the author are the main sources of this study.

The paper recalls the processes of cultural appropriation that take place when people adjust to new cultural contexts. In the case of the dwellings studied, these processes give clues on how the ideas that shape the way people use space are translated into new places. The paper's conclusion calls for further research on EUD as an object of academic interest.

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP.

Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta.

Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance.

This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems.

The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing.

The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

This paper aims to introduce a new framework that helps to get an overview of contextual factors that influence the ability of small- and medium-sized enterprises (SMEs) to survive the economic crisis in a business cluster, as parts of a system.

The spatial autologistic model and the logit regression tree (RT) were applied to SME manufacturing companies localized in the business clusters of the Italian Marche region to explain interconnection among the actors of the network and their heterogeneous behavior with the environment.

The main findings of the application confirm that contextual influences are decisive in the definition of firm’s survival, explained through the presence of spatial dependence in bankruptcy analysis, validating the transmission effects of corporate bankruptcy within the business clusters in the Marche region.

The estimation of the logistic RT allowed to identify sub-systems, homogeneous with respect to crucial context variables, with different firms’ behaviors in terms of probability to survive in the system and relation to their environment. Therefore, a systemic approach is required to provide a better understanding of such kind of phenomena.

The Ba River catchment and delta on the island of Viti Levu, Fiji, supports a wealth of livelihoods and is populated by diverse communities who are living with an increased frequency and intensity of hydro-meteorological hazards (floods, cyclones and droughts). Participatory mapping as part of focus group discussions is a tool that can be used to elucidate communities’ understanding of the differing impacts of multiple hazards, as well as the strategies used to prepare and respond to different hazards. In this chapter, the authors present the results of qualitative research undertaken with members of three communities along the Ba River, from the Nausori highlands to the coastal mangroves, with a particular focus on recent floods (2009, 2012) and Tropical Cyclone Winston (2016). The communities draw on a wide range of livelihood strategies from fishing and agriculture to tourism and outside work. Natural hazard events vary in their impact on these livelihood strategies across the landscape and seascape, so that community members can adjust their activities accordingly. The temporal ‘signatures’ of ongoing impacts are also variable across communities and resources. The results suggest that taking a broad, landscape (and seascape) approach to understanding how communities draw livelihoods is valuable in informing effective and inclusive adaptation strategies for environmental change. Furthermore, documenting how the landscape is used in a mapped output may be a valuable tool for future social impact assessment for resource extraction activities.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

A major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval.

The proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively.

The approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included.

The capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.

Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions.

This sample is composed by the data of personal loans transactions of a Brazilian credit union.

The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making.

Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.