Learning to Create - Creating to Learn

Creativity has been recognized as a fundamental future competence, but there has been a lack of research on pedagogies in nurturing learners’ creativity in K–12 education. This chapter explores how students’ and teachers’ competencies for creativity can be applied and developed through participation in invention projects. An invention project called We Design & Make, in which eighth-grade students used the design thinking approach for co-creating e-textile products for local preschoolers, is presented. By describing the nature of their creative process and practices, and the teachers’ pedagogical practices for building a creative classroom culture, how competencies for creativity include various skills and capabilities that concern both students and teachers is illustrated. These include creative and critical thinking skills, social and emotional skills, and certain domain-specific concepts and practices.Peer reviewe

Deep learning for forest inventory and planning : a critical review on the remote sensing approaches so far and prospects for further applications

Data processing for forestry applications is challenged by the increasing availability of multi-source and multi-temporal data. The advancements of Deep Learning (DL) algorithms have made it a prominent family of methods for machine learning and artificial intelligence. This review determines the current state-of-the-art in using DL for solving forestry problems. Although DL has shown potential for various estimation tasks, the applications of DL to forestry are in their infancy. The main study line has related to comparing various Convolutional Neural Network (CNN) architectures between each other and against more shallow machine learning techniques. The main asset of DL is the possibility to internally learn multi-scale features without an explicit feature extraction step, which many people typically perceive as a black box approach. According to a comprehensive literature review, we identified challenges related to (1) acquiring sufficient amounts of representative and labelled training data, (2) difficulties to select suitable DL architecture and hyperparameterization among many methodological choices and (3) susceptibility to overlearn the training data and consequent risks related to the generalizability of the predictions, which can however be reduced by proper choices on the above. We recognized possibilities in building time-series prediction strategies upon Recurrent Neural Network architectures and, more generally, re-thinking forestry applications in terms of components inherent to DL. Nevertheless, DL applications remain data-driven, in contrast to being based on causal reasoning, and currently lack many best practices of conventional forestry modelling approaches. The benefits of DL depend on the application, and the practitioners are advised to ex ante subject their requirements to operational data availability, for example. By this review, we contribute to the technical discussion about the prospects of DL for forestry and shed light on properties that require attention from the practitioners.Peer reviewe

Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space

Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry.Peer reviewe

Sources and types of uncertainties in the information on forest-related ecosystem services

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Hukkakauran torjunnassa ei saa nuukailla

Hukkakaura lisääntyy pelloillamme pääosin torjunnan laiminlyömisen vuoksi. Markkinoilla olevat hukkakauran torjunta-aineet ovat tehokkaita, mutta niiden käytössä ei saa tinkiä. Epäedullisissa ruiskutusolosuhteissa ja suorakylvetyillä tai kevytmuokatuilla viljalohkoilla tulee käyttää suurinta suositeltua torjunta-aineannosta.vo

Comparative Numerical Study on the Weakening Effects of Microwave Irradiation and Surface Flux Heating Pretreatments in Comminution of Granite

Thermal pretreatments of rock, such as conventional heating and microwave irradiation, have received considerable attention recently as a viable method of improving the energy efficiency of mining processes that involve rock fracturing. This study presents a numerical analysis of the effects of thermal shock and microwave heating on the mechanical properties of hard, granite-like rock. More specifically, the aim is to numerically assess the reduction of uniaxial compressive strength of thermally pretreated specimens compared to intact ones. We also compare the performance of these two pretreatments (conventional heating and microwave irradiation) in terms of consumed energy and induced damage. Rock fracture is modelled by a damage-viscoplasticity model, with separate damage variables in tension and compression. A global solution strategy is developed for solving the thermo-mechanical problem (conventional heating) and the electromagnetic–thermo-mechanical problem (microwave heating). The electromagnetic part of the microwave heating problem is solved in COMSOL Multiphysics software Version 6.1 first. The electromagnetic solution is used as an input for the thermo-mechanical problem, which is finally solved by means of a staggered explicit solution method. Due to the predominance of the external thermal sources, the thermal and the mechanical parts of the problem in both cases are considered as uncoupled. Three-dimensional finite element simulations are utilized to study the damage-viscoplasticity model. An ore-shaped three-mineral numerical rock specimen is used in uniaxial compression tests.publishedVersionPeer reviewe