Traditional environmental knowledge and community perceptions in sustaining the forest environment : case study of Minahasa ethnic community in the surrounding of Lake Tondano, North Sulawesi - Indonesia

The idea of sustainable development is distinct from the idea of restoring or conserving nature. This concept is embedded in the Rio Declaration on Environment and Development (1992) which Indonesia and several countries around the world have signed. Sustainable development seeks to interlace humans and nature, while restoration (especially at the large scale) often allows nature to be addressed separately, sometimes out of remorse for the damage caused by humans. In terms of attaining sustainable natural resource development, the opportunities offered by traditional ecological knowledge documentation are considered essential in enabling the achievement of sustainability because most of these Indigenous and/or local communities are situated in areas where many species have been historically cultivated and used in a sustainable way for thousands of years. The skill and techniques of these local communities can provide valuable information for the global community to evaluate current environmental policies. Such research and evaluation is often robustly and best undertaken through ethnoecological methodological paradigms. This paper examines the traditional environment knowledge of the Minahasan ethnic community, who live in the surrounds of Lake Tondano in the North Sulawesi, together with the Minahasan conscious and unconscious actions in conserving their forest ecology in addition to their knowledge of culture about forest protection in the region. In particular, contemporary use of traditional environmental knowledge is examined in terms of its relevance to in traditional resource management and land use planning, as avenues to better curate and manage natural resources through informed regional planning strategies and mechanisms.

Deeper than the forest AMBIENT

A short instrumental featuring percussion, bells, synths, mandolin, and sound design.

Deeper than the forest AMBIENT with VOCAL

A short instrumental featuring ethereal female vocals, percussion, bells, synths, mandolin, and sound design.

Ambient forest 30 sec

An ambient, new age style short composition featuring; African percussion and synths; 30 seconds in length.

Encouraging play in the natural environment : a child-focused case study of Forest School

There is concern that children are becoming disengaged from the natural environment and are not being afforded the opportunities to play in such environments. To examine children's perceptions, knowledge and experiences of play in the natural environment, 17 children from one school participated in small focus groups before and after a 12-week Forest School that took place within a school woodland area. Using two qualitative approaches, we found that Forest School had a positive influence on children's natural play and their knowledge of the natural world around them.

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

All the forest's a bug's stage

Review: Cirque du Soleil's OVO

Network traffic clustering using random forest proximities

The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for the task, which performs clustering based on Random Forest (RF) proximities instead of Euclidean distances. The approach consists of two steps. In the first step, we derive a proximity measure for each pair of data points by performing a RF classification on the original data and a set of synthetic data. In the next step, we perform a K-Medoids clustering to partition the data points into K groups based on the proximity matrix. Evaluations have been conducted on real-world Internet traffic traces and the experimental results indicate that the proposed approach is more accurate than the previous methods.