Characterization of Smallholder Beef Cattle Production System in Central Vietnam –Revealing Performance, Trends, Constraints, and Future Development

The objective of this study is to evaluate the characteristics of smallholder beef cattle production in Central Vietnam. A total of 360 households were interviewed by using semi-structured questionnaire; a total of 606 beef cows were investigated for evaluating calving interval (CI). Thirty-two fattening cattle were monitored for the estimation of diet structure. Results showed that the cattle herd size was 4.32-4.45 cattle/household. In North Central (NC), 55% of surveyed farmers kept local cattle, 45% kept crossbreeds, and none of surveyed farmers keeping exotic breeds. In South Central (SC), 63% of surveyed farmers kept cross cattle, 32% kept local cattle, and 5% kept exotic breeds. In the breeding method, 70% of surveyed farmers used artificial insemination (AI), 20% used natural mating (NM), and only 10% used both AI and NM in SC, whereas in NC 40% of farmers used AI, 40% used NM, and 20% used both AI and NM. The variety of feedstuffs fed to cattle including roughages and concentrate. The concentrate in the diet for fattening cattle was 25%-35% and protein level was 11%-13%, and the average daily gain of cattle was 0.51-0.63 kg/day. The CI of cows was 12-13 months in SC, whereas in NC it was 13-14 months. There were numerous constraints to cattle production in surveyed households including diseases, lack of good quality feed sources, breeds, knowledge, and lack of capital. In conclusion, cattle production in Central Vietnam is small scale and still largely extensive. There are constraints that must be solved to improve livestock systems in the near future, especially when shifting towards semi-intensive and/or intensive cattle production systems.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Objectives: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Setting: A regional cancer centre in Australia. Participants: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data. Primary and secondary outcome measures: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC). Results: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours. Conclusions: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems

Transport Phenomena and Structuring in Shear Flow of Suspensions near Solid Walls

In this paper we apply the lattice-Boltzmann method and an extension to particle suspensions as introduced by Ladd et al. to study transport phenomena and structuring effects of particles suspended in a fluid near sheared solid walls. We find that a particle free region arises near walls, which has a width depending on the shear rate and the particle concentration. The wall causes the formation of parallel particle layers at low concentrations, where the number of particles per layer decreases with increasing distance to the wall.Comment: 14 pages, 14 figure