Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against

Plasma phyto-oestrogens and prostate cancer in the European Prospective Investigation into Cancer and Nutrition

We examined plasma concentrations of phyto-oestrogens in relation to risk for subsequent prostate cancer in a case–control study nested in the European Prospective Investigation into Cancer and Nutrition. Concentrations of isoflavones genistein, daidzein and equol, and that of lignans enterolactone and enterodiol, were measured in plasma samples for 950 prostate cancer cases and 1042 matched control participants. Relative risks (RRs) for prostate cancer in relation to plasma concentrations of these phyto-oestrogens were estimated by conditional logistic regression. Higher plasma concentrations of genistein were associated with lower risk of prostate cancer: RR among men in the highest vs the lowest fifth, 0.71 (95% confidence interval (CI) 0.53–0.96, P trend=0.03). After adjustment for potential confounders this RR was 0.74 (95% CI 0.54–1.00, P trend=0.05). No statistically significant associations were observed for circulating concentrations of daidzein, equol, enterolactone or enterodiol in relation to overall risk for prostate cancer. There was no evidence of heterogeneity in these results by age at blood collection or country of recruitment, nor by cancer stage or grade. These results suggest that higher concentrations of circulating genistein may reduce the risk of prostate cancer but do not support an association with plasma lignans

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Modeling travel mode choice and characterizing freight transport in a Brazilian context

Freight transportation in Brazil is characterized by the predominance of the road mode. This imbalance in the sector suggests the need to develop efficient strategies that can increase competitiveness of alternative modes . However, in Brazil, there are few studies investigating firms’ preferences concerning different attributes of travel modes. This study analyses the travel mode choice decision-making process of shippers in the state of Rio de Janeiro, Brazil. The main objectives of this article are related to model travel mode choice and characterize freight transport in a Brazilian context. Discrete choice models were estimated using Stated Preference data to identify shippers’ preferences and discuss some possible sustainable policies that could increase the competitiveness of the railway network. Elasticities and probability marginal effects were computed, and different scenarios were simulated to predict the possible effects of implementing alternative transport policies. Simulation results show that shippers’ preferences have low sensitivity to changing factors

Application of MDCEV to infrastructure planning in regional freight transport

The main objective of the paper is to develop a model capable of evaluating the societal impact of rail infrastructure investment in Argentina, using a Multiple Discrete Extreme Value Model (MDCEV) estimated on Stated and Revealed preference data. The decision modelled is the mode and port choice at a planning level, where multiple alternatives can be chosen simultaneously. The relevant variables were the Free Alongside Ship (FAS) price, freight transport cost, travel time and lead time, including non-observed heterogeneity in the modelling. As a consequence, the willingness to pay measures that are used for the cost benefit analysis become non-deterministic. To include this effect simulated WTP measurements were included and compared to a deterministic and risk based approach. Two projects were tested and both showed that the deterministic approach gives higher Benefit/Cost ratio. This paper raises the concern that if non-observed heterogeneity is not considered in project evaluation it may provide misleading results and potentially lead to wrong investment priorities for the public sector

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19