The use of ecosystem-based adaptation practices by smallholder farmers in Central America

There is growing interest in promoting the use of Ecosystem-based Adaptation (EbA) practices to help smallholder farmers adapt to climate change, however there is limited information on how commonly these practices are used by smallholder farmers and what factors influence their use. Using participatory mapping and field surveys, we examined the prevalence and characteristics of EbA practices on 300 smallholder coffee and maize farmers in six landscapes in Central America and explored the socioeconomic and biophysical factors associated with their use. The prevalence of individual EbA practices varied across smallholder farms. Common EbA practices included live fences, home gardens, shade trees in coffee plantations, and dispersed trees in maize fields. We found a mean of 3.8 EbA practices per farm. Factors that were correlated with the total number of EbA practices on farms included the mean area of coffee plantations, farmer age, farmer experience, the farm type and the landscape in which farms were located. Factors associated with the presence or characteristics of individual EbA practices included the size of coffee plantations, farmer experience, farmer education, land tenure, landscape and farm type. Our analysis suggests that many smallholder farmers in Central America are already using certain EbA practices, but there is still scope for greater implementation. Policy makers, donors and technicians can encourage the broader use of EbA by smallholder farmers by facilitating farmer-to-farmer exchanges to share knowledge on EbA implementation, assessing the effectiveness of EbA practices in delivering adaptation benefits, and tailoring EbA policies and programs for smallholder farmers in different socioeconomic and biophysical contexts. (Résumé d'auteur

A middle time recognition of epileptic seizures from geometrical patterns of EEG data

An approach for middle- time recognition of epileptic seizures from EEG data is proposed. The method considers sharp changes in the recorded data using geometrical patterns of the signal in phase-space. The approach was developed using experimental clinical EEG data recorded from ten patients and reliably predicted epileptic seizures in the ten-minute interval before the seizure onsets. An estimation of sensitivity and specificity of the proposed method is also provided.Запропоновано підхід до передбачення епілептичних припадків з ЕЕГ даних на середньотермінових інтервалах. Метод вивчає різкі зміни в отриманих даних використовуючи геометричну картину сигналу в фазовому просторі. Підхід развинено на основі використання реальних клінічних ЕЕГ даних, що записані у десяти пацієнтів, і показано передбачення епілептичних припадків за час до десяти хвилин перед припадком. Запропоновані також оцінки чутливості та особливостей запропонованого підходу.Предложен подход для предсказания эпилептических припадков из ЭЭГ данных на средневременных интервалах. Метод изучает резкие изменения в полученных данных используя геометрическую картину сигнала в фазовом пространстве. Подход развит на основе использования реальных клинических ЭЭГ данных записанных у десяти пациентов и показал предсказание эпилептических припадков за время до десяти минут перед припадком. Предложены также оценки чувствительности и особенностей предложенного подхода