Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Advances in mesoporous molecular sieve MCM-41

The discovery of mesoporous molecular sieves, MCM-41, which possesses a regular hexagonal array of uniform pore openings, aroused a worldwide resurgence in this field. This is not only because it has brought about a series of novel mesoporous materials with various compositions which may find applications in catalysis, adsorption, and guest-host chemistry, but also it has opened a new avenue for creating zeotype materials. This paper presents a comprehensive overview of recent advances in the field of MCM-41. Beginning with the chemistry of surfactant/silicate solutions, progresses made in design and synthesis, characterization, and physicochemical property evaluation of MCM-41 are enumerated. Proposed formation mechanisms are presented, discussed, and identified. Potential applications are reviewed and projected. More than 100 references are cited.

A shift in priority in diabetic foot care and research: 75% of foot ulcers are preventable

Diabetic foot ulceration poses a heavy burden on the patient and the healthcare system, but prevention thereof receives little attention. For every euro spent on ulcer prevention, ten are spent on ulcer healing, and for every randomized controlled trial conducted on prevention, ten are conducted on healing. In this article, we argue that a shift in priorities is needed. For the prevention of a first foot ulcer, we need more insight into the effect of interventions and practices already applied globally in many settings. This requires systematic recording of interventions and outcomes, and well-designed randomized controlled trials that include analysis of cost-effectiveness. After healing of a foot ulcer, the risk of recurrence is high. For the prevention of a recurrent foot ulcer, home monitoring of foot temperature, pressure-relieving therapeutic footwear, and certain surgical interventions prove to be effective. The median effect size found in a total of 23 studies on these interventions is large, over 60%, and further increases when patients are adherent to treatment. These interventions should be investigated for efficacy as a state-of-the-art integrated foot care approach, where attempts are made to assure treatment adherence. Effect sizes of 75-80% may be expected. If such state-of-the-art integrated foot care is implemented, the majority of problems with foot ulcer recurrence in diabetes can be resolved. It is therefore time to act and to set a new target in diabetic foot care. This target is to reduce foot ulcer incidence with at least 75%.