Livelihood and Community Development: LforS learning and simulation game

Up to 15 people can participate in the game, which is supervised by a moderator. Households consisting of 1-5 people discuss options for diversification of household strategies. Aim of the game: By devising appropriate strategies, households seek to stand up to various types of events while improving their economic and social situation and, at the same time, taking account of ecological conditions. The annual General Community Meeting (GCM) provides an opportunity for households to create a general set-up at the local level that is more or less favourable to the strategies they are pursuing. The development of a community investment strategy, to be implemented by the GCM, and successful coordination between households will allow players to optimise their investments at the household level. The household who owns the most assets at the end of the game wins. Players participate very actively, as the game stimulates lively and interesting discussions. They find themselves confronted with different types of decision-making related to the reality of their daily lives. They explore different ways to model their own household strategies and discuss risks and opportunities. Reflections on the course of the game continually refer to the real-life situations of the participants.

Sustainable Pasture Management : LforS learning and simulation game

Three teams consisting of 2 to 5 persons each play the game. Each team represents a farm. Each team decides jointly on its strategy. In annual meetings in winter, the farm teams jointly discuss, evaluate and decide on how to proceed and actions to be taken. The farms make use of three different pasture areas (village pasture, intensive pasture and summer pasture) for grazing their livestock. The carrying capacity of each pasture area is different and varies according to the season. In each season, the farms have to decide on how many livestock units to graze on which pasture. Overgrazing and pasture degradation occur if the total number of livestock units exceeds the carrying capacity of a specific pasture area. Overgrazing results in a reduction of pasture productivity. To diversify and improve their livelihood strategy farms can make individual investments to increase productivity at the farm level, eg. in fodder production or in income generating activities. At the community level, collective investments can be made which may influence livestock and household economy, e.g. rehabilitate and improve pasture productivity, improve living conditions on remote pastures etc. Events occurring in the course of the game represent different types of (risk) factors such as meteorology, market, politics etc. that may positively or negatively influence livestock production and household economy. A sustainable management of pastures requires that farms actively regulate the development of their herds, that they take measures to prevent pasture degradation and to improve pasture productivity, and that they find a balance between livestock economy and other productive activities. The game has a double aim: a) each farm aims at its economic success and prosperity, and b) the three farm teams jointly have to find and implement strategies for a sustainable use of pasture areas.

Integrated Disaster Reduction Approach: LforS Learning and Simulation Game

Main objective of the game is to increase the coping capacity of players and familiarise them with the Integrated Disaster Reduction Approach. The game is intended to prepare for and introduce the players to a subsequent Learning for Sustainability capacity building workshop for community leaders. The game represents a typical emergency situation resulting from a natural disaster. Before and after the event, adequate measures help to prevent or minimise potential damages. Once a disaster has occurred, concerted actions and immediate measures need to be taken to rescue as much as possible (human lives, livestock, material) and safeguard the village against further damage and losses. In the course of the game, each playing team can proof its knowledge on adequate measures that have to be taken in order to avoid or reduce losses related to natural disasters. Such measures relate to assessment and monitoring of risks, prevention and mitigation measures, preparedness and response as well as recovery and reconstruction.

Stratégies de ménages et développement communal: LforS learning and simulation game

Au travers de stratégies appropriées, les ménages, appelés ici Unités de Production et de Consommation (UPC), cherchent à faire face à différents évènements et à améliorer leur situation économique et sociale tout en tenant compte des conditions écologiques. Au travers de l’Assemblée Générale Communale (AGC), les UPC peuvent créer des conditions cadres locales plus ou moins favorables aux stratégies qu’ils poursuivent. Par le développement d’une stratégie d’investissement communale mise en œuvre par l’AGC et une bonne coordination entre les UPC, les joueurs peuvent optimiser leurs investissements au niveau des ménages. Vainqueur est l’UPC qui à la fin du jeu dispose du plus grand patrimoine.

CONMICOM – conflict mitigation in communities: LforS learning and simulation game

Three extended families live around a lake. One family are rice farmers, the second family are vegetable farmers, and the third are a family of livestock herders. All of them depend on the use of lake water for their production, and all of them need large quantities of water. All are dependent on the use of the lake water to secure their livelihood. In the game, the families are represented by their councils of elders. Each of the councils has to find means and ways to increase production in order to keep up with the growth of its family and their demands. This puts more and more pressure on the water resources, increasing the risk of overuse. Conflicts over water are about to emerge between the families. Each council of elders must try to pursue its families interests, while at the same time preventing excessive pressure on the water resources. Once a council of elders is no longer able to meet the needs of its family, it is excluded from the game. Will the parties cooperate or compete? To face the challenge of balancing economic well-being, sustainable resource management, and individual and collective interests, the three parties have a set of options for action at hand. These include power play to safeguard their own interests, communication and cooperation to negotiate with neighbours, and searching for alternatives to reduce pressure on existing water resources. During the game the players can experience how tensions may arise, increase and finally escalate. They realise what impact power play has and how alliances form, and the importance of trust-building measures, consensus and cooperation. From the insights gained, important conflict prevention and mitigation measures are derived in a debriefing session. The game is facilitated by a moderator, and lasts for 3-4 hours. Aim of the game: Each family pursues the objective of serving its own interests and securing its position through appropriate strategies and skilful negotiation, while at the same time optimising use of the water resources in a way that prevents their degradation. The end of the game is open. While the game may end by one or two families dropping out because they can no longer secure their subsistence, it is also possible that the three families succeed in creating a situation that allows them to meet their own needs as well as the requirements for sustainable water use in the long term. Learning objectives The game demonstrates how tension builds up, increases, and finally escalates; it shows how power positions work and alliances are formed; and it enables the players to experience the great significance of mutual agreement and cooperation. During the game and particularly during the debriefing and evaluation session it is important to link experiences made during the game to the players’ real-life experiences, and to discuss these links in the group. The resulting insights will provide a basis for deducing important conflict prevention and transformation measures.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Building the capacity for learning and change through reflective conversation

Conversation is central to the process of organizational learning and change. Drawing on the notion of reflective conversation, we describe an action research project, "learning through listening" in Omega, a residential healthcare organization. In this project, service users, staff, members of management committees, trustees, managers, and central office staff participated in listening to each other and in working together towards building capacity for creating their own vision of how the organization could move into the future, according to its values and ethos. In doing so they developed ways of engaging in reflective conversation that enabled progress towards a strategic direction.

How Transcranial Direct Current Stimulation Can Modulate Implicit Motor Sequence Learning and Consolidation: A Brief Review

The purpose of this review is to investigate how transcranial direct current stimulation(tDCS)can modulate implicit motor sequence learning and consolidation. So far, most of the studies have focused on the modulating effect of tDCS for explicit motor learning. Here, we focus explicitly on implicit motor sequence learning and consolidation in order to improve our understanding about the potential of tDCS to affect this kind of unconscious learning. Specifically, we concentrate on studies with the serial reaction time task (SRTT), the classical paradigm for measuring implicit motor sequence learning. The influence of tDCS has been investigated for the primary motor cortex, the premotor cortex, the prefrontal cortex, and the cerebellum. The results indicate that tDCS above the primary motor cortex gives raise to the most consistent modulating effects for both implicit motor sequence learning and consolidation.

Effects of Nosema apis, N. ceranae, and coinfections on honey bee (Apis mellifera) learning and memory

Western honey bees (Apis mellifera) face an increasing number of challenges that in recent years have led to significant economic effects on apiculture, with attendant consequences for agriculture. Nosemosis is a fungal infection of honey bees caused by either Nosema apis or N. ceranae. The putative greater virulence of N. ceranae has spurred interest in understanding how it differs from N. apis. Little is known of effects of N. apis or N. ceranae on honey bee learning and memory. Following a Pavlovian model that relies on the proboscis extension reflex, we compared acquisition learning and long-term memory recall of uninfected (control) honey bees versus those inoculated with N. apis, N. ceranae, or both. We also tested whether spore intensity was associated with variation in learning and memory. Neither learning nor memory differed among treatments. There was no evidence of a relationship between spore intensity and learning, and only limited evidence of a negative effect on memory; this occurred only in the co-inoculation treatment. Our results suggest that if Nosema spp. are contributing to unusually high colony losses in recent years, the mechanism by which they may affect honey bees is probably not related to effects on learning or memory, at least as assessed by the proboscis extension reflex.

A case study of teachers' mathematics content knowledge and attitudes toward mathematics and teaching

This study intended to measure teacher mathematical content knowledge both before and after the first year of teaching and taking graduate teacher education courses in the Teach for America (TFA) program, as well as measure attitudes toward mathematics and teaching both before and after TFA teachers’ first year. There was a significant increase in both mathematical content knowledge and attitudes toward mathematics over the TFA teachers’ first year teaching. Additionally, several significant correlations were found between attitudes toward mathematics and content knowledge. Finally, after a year of teaching, TFA teachers had significantly better attitudes toward mathematics and teaching than neutral.

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

Measuring the influence of Cooperative Learning and Project Based Learning on problem solvin skill.

The aim of this study is to evaluate the effects obtained after applying two active learning methodologies (cooperative learning and project based learning) to the achievement of the competence problem solving. This study was carried out at the Technical University of Madrid, where these methodologies were applied to two Operating Systems courses. The first hypothesis tested was whether the implementation of active learning methodologies favours the achievement of ?problem solving?. The second hypothesis was focused on testing if students with higher rates in problem solving competence obtain better results in their academic performance. The results indicated that active learning methodologies do not produce any significant change in the generic competence ?problem solving? during the period analysed. Concerning this, we consider that students should work with these methodologies for a longer period, besides having a specific training. Nevertheless, a close correlation between problem solving self appraisal and academic performance has been detected.