Background: Despite the benefits related to physical exercise, large numbers of cancer patients are not sufficiently active. Methods: To investigate exercise levels and preferences in cancer patients, a cross-sectional study was conducted on a random sample of 392 cancer outpatients who anonymously completed a questionnaire investigating general and medical characteristics, and expressed willingness to participate in exercise programs. Current exercise levels were estimated with the Leisure Score Index (LSI). Results: Most patients (93%) were insufficiently active but 80% declared an interest in exercise programs. Patients preferred oncologist-instructed programs and specified particular exercise needs. Multivariate logistic regression showed that willingness to exercise was associated with education (OR: 1.87; 95% CI: 1.15–3.04 beyond age 14 years vs. up to 14 years) and current physical activity (OR: 1.92; 95% CI: 1.92–3.63 for sweat-inducing activity >2 times/week vs. <1 time/week). Patients given chemotherapy were less inclined to exercise (OR: 0.45; 95% CI: 0.23–0.86) than those who did not. LSI was lower if cancer stage was advanced (β: -0.36; 95% CI: −0.75 to −0.02) than if it was in remission. High LSI was also associated with longer education, lower BMI, and longer time after diagnosis. Conclusion: Cancer patients are insufficiently active but are willing to participate in personalized exercise programs. Information from this survey may help in designing personalized interventions so these patients will achieve sufficient exercise.

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Direct marketing campaigns are one of the main fundraising sources for nonprofit organizations and their effectiveness is crucial for the sustainability of the organizations. The response rate of these campaigns is the result of the complex interaction between several factors, such as the theme of the campaign, the month in which the campaign is launched, the history of past donations from the potential donor, as well as several other variables. This work, applied on relevant data gathered from the World Wide Fund for Nature Italian marketing department, undertakes different data mining approaches in order to predict future donors and non-donors, thus allowing for optimization in the target selection for future campaigns, reducing its overall costs. The main challenge of this research is the presence of thoroughly imbalanced classes, given the low percentage of responses per total items sent. Different techniques that tackle this problem have been applied. Their effectiveness in avoiding a biased classification, which is normally tilted in favor of the most populated class, will be highlighted. Finally, this work shows and compares the classification results obtained with the combination of sampling techniques and Decision Trees, ensemble methods, and Artificial Neural Networks. The testing approach follows a walkforward validation procedure, which simulates a production environment and reveals the ability to accurately classify each future campaign.

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In this paper we focus on the linear functionals defining an approximate version of the gradient of a function. These functionals are often used when dealing with optimization problems where the computation of the gradient of the objective function is costly or the objective function values are affected by some noise. These functionals have been recently considered to estimate the gradient of the objective function by the expected value of the function variations in the space of directions. The expected value is then approximated by a sample average over a proper (random) choice of sample directions in the domain of integration. In this way the approximation error is characterized by statistical properties of the sample average estimate, typically its variance. Therefore, while useful and attractive bounds for the error