Internet-based treatment of gambling problems: a systematic review and meta-analysis of randomized controlled trials

Background and aims: The effect of internet-based psychological treatment for gambling problems has not been previously investigated by meta-analysis. The present study is therefore a quantitative synthesis of studies on the effects of internet-based treatment for gambling problems. Given that effects may vary according to the presence of therapist support and control conditions, it was presumed that subgroup analyses would elucidate such effects. Methods: A systematic search with no time constraints was conducted in PsycINFO, MEDLINE, Web of Science, and the Cochrane Library. Two authors independently extracted data using a predefined form, including study quality assessment based on the Cochrane risk of bias tool. Effect sizes were calculated using random-effects models. Heterogeneity was indexed by Cochran’s Q and the I2 statistics. Publication bias was investigated using trim and fill. Results: Thirteen studies were included in the analysis. Random effects models at post-treatment showed significant effects for general gambling symptoms (g = 0.73; 95% CI = 0.43–1.03), gambling frequency (g = 0.29; 95% CI = 0.14–0.45), and amount of money lost gambling (g = 0.19; 95% CI = 0.11–0.27). The corresponding findings at follow-up were g = 1.20 (95% CI = 0.79–1.61), g = 0.36 (95% CI = 0.12–0.60), and g = 0.20 (95% CI = 0.12–0.29) respectively. Subgroup analyses showed that for general gambling symptoms, studies with therapist support yield larger effects than studies without, both post-treatment and at follow-up. Additionally, on general gambling symptoms and gambling frequency, there were lower effect sizes for studies with a control group compared to studies without a control group at follow-up. Studies with higher baseline severity of gambling problems were associated with larger effect sizes at both posttreatment and follow-up than studies with more lenient inclusion criteria concerning gambling problems. Discussion and conclusions: Internet-based treatment has the potential to reach a large proportion of persons with gambling problems. Results of the meta-analysis suggest that such treatments hold promise as an effective approach. Future studies are encouraged to examine moderators of treatment outcomes, validate treatment effects cross-culturally, and investigate the effects of novel developments such as ecological momentary interventions

Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior

Ice material models often limit the accuracy of ice related simulations. The reasons for this are manifold, e.g. complex ice properties. One issue is linking experimental data to ice material modeling, where the aim is to identify patterns in the data that can be used by the models. However, numerous parameters that influence ice behavior lead to large, high dimensional data sets which are often fragmented. Handling the data manually becomes impractical. Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental database is established

Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation

Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor

Computational shelf-life dating : complex systems approaches to food quality and safety

Shelf-life is defined as the time that a product is acceptable and meets the consumers expectations regarding food quality. It is the result of the conjunction of all services in production, distribution, and consumption. Shelf-life dating is one of the most difficult tasks in food engineering. Market pressure has lead to the implementation of shelf-life by sensory analyses, which may not reflect the full quality spectra. Moreover, traditional methods for shelf-life dating and small-scale distribution chain tests cannot reproduce in a laboratory the real conditions of storage, distribution, and consumption on food quality. Today, food engineers are facing the challenges to monitor, diagnose, and control the quality and safety of food products. The advent of nanotechnology, multivariate sensors, information systems, and complex systems will revolutionize the way we manage, distribute, and consume foods. The informed consumer demands foods, under the legal standards, at low cost, high standards of nutritional, sensory, and health benefits. To accommodate the new paradigms, we herein present a critical review of shelf-life dating approaches with special emphasis in computational systems and future trends on complex systems methodologies applied to the prediction of food quality and safety.Fundo Europeu de Desenvolvimento Regional (FEDER) - Programa POS-ConhecimentoFundação para a Ciência e a Tecnologia (FCT) - SFRH/BPD/26133/2005, SFRH/ BPD/20735/200

A heuristic for moment-matching scenario generation

In stochastic programming models we always face the problem of how to represent the random variables. This is particularly difficult with multidimensional distributions. We present an algorithm that produces a discrete joint distribution consistent with specified values of the first four marginal moments and correlations. The joint distribution is constructed by decomposing the multivariate problem into univariate ones, and using an iterative procedure that combines simulation, Cholesky decomposition and various transformations to achieve the correct correlations without changing the marginal moments. With the algorithm, we can generate 1000 one-period scenarios for 12 random variables in 16 seconds, and for 20 random variables in 48 seconds, on a Pentium III machine