Rethinking Education for Sustainability

2015 ◽  
pp. 123-141
Author(s):  
Leonardo Giusti ◽  
Alessandro Pollini ◽  
Federico Casalegno
Keyword(s):  
Learning Objectives ◽  
Mobile Technologies ◽  
Education For Sustainability ◽  
Outdoor Learning ◽  
Pedagogical Model ◽  
First Case ◽  
Educational Challenges ◽  
Technological Platform ◽  
Rethinking Education

This chapter presents a pedagogical model and a technological platform consisting of web and mobile technologies designed to support a mix of formal and informal, indoor and outdoor learning experiences. In particular, the platform is a reconfigurable system that can be adapted to support different kinds of learning formats. Two case studies will be presented to describe how the proposed pedagogical model and the technological platform can be adapted to address different contexts and learning objectives. The first case study – H2Flow – has been carried out in an high-school in Trento (north Italy) as an extension of the school curricula; the second one – Youth Mapping – has been deployed in underserved areas in Rio De Janeiro as part of a community-driven initiative led by UNICEF. In the conclusion, we discuss educational challenges and design opportunities concerning the use of mobile technologies in the context of education on sustainable development.

Rethinking Education for Sustainability

2015 ◽  
pp. 1987-2004
Author(s):  
Leonardo Giusti ◽  
Alessandro Pollini ◽  
Federico Casalegno
Keyword(s):  
Learning Objectives ◽  
Mobile Technologies ◽  
Education For Sustainability ◽  
Outdoor Learning ◽  
Pedagogical Model ◽  
First Case ◽  
Educational Challenges ◽  
Technological Platform ◽  
Rethinking Education

This chapter presents a pedagogical model and a technological platform consisting of web and mobile technologies designed to support a mix of formal and informal, indoor and outdoor learning experiences. In particular, the platform is a reconfigurable system that can be adapted to support different kinds of learning formats. Two case studies will be presented to describe how the proposed pedagogical model and the technological platform can be adapted to address different contexts and learning objectives. The first case study – H2Flow – has been carried out in an high-school in Trento (north Italy) as an extension of the school curricula; the second one – Youth Mapping – has been deployed in underserved areas in Rio De Janeiro as part of a community-driven initiative led by UNICEF. In the conclusion, we discuss educational challenges and design opportunities concerning the use of mobile technologies in the context of education on sustainable development.

En Plein Air

2012 ◽  
Vol 4 (2) ◽  
pp. 44-58 ◽  
Author(s):  
Leonardo Giusti ◽  
Alessandro Pollini ◽  
Liselott Brunnberg ◽  
Federico Casalegno
Keyword(s):  
Sustainable Development ◽  
Learning Experiences ◽  
Use Cases ◽  
Mobile Technologies ◽  
Outdoor Learning ◽  
Pedagogical Model ◽  
Reconfigurable System ◽  
Educational Challenges ◽  
Technological Platform ◽  
Indoor And Outdoor

This paper discusses educational challenges and design opportunities concerning the use of mobile technologies in the context of education on sustainable development. The discussion will be supported by the presentation of a pedagogical model and a technological platform consisting of Web and mobile technologies designed to support a mix of formal and informal, indoor and outdoor learning experiences. In particular, the platform is a reconfigurable system that can be adapted to support different kinds of learning formats. The paper presents two use cases and the authors discuss the implications of mobile technologies in the field of education for sustainable development, taking into consideration both pedagogical and technological issues.

EDUCATIONAL CHALLENGES AS ACTIVITY MOTIVATORS

2019 ◽  
pp. 2-7
Author(s):  
N. V. Brendina
Keyword(s):  
Student Feedback ◽  
Mobile Technologies ◽  
Educational Challenges ◽  
Cognitive Motivation

The article describes modern motivational schemes aimed at the initiation, formation and development of learning and cognitive motivation of students. The schemes were developed using elements of gamification based on mobile technologies, which made it possible to increase the overall involvement of students in the search for solutions to the problems posed. The didactic potential of the games-сhallenges is considered. The structure of the challenge "Explanation", and stages of a QR-quest are presented. The model is concretized by educational products and student feedback, successfully tested.

Motivating Growth in Low-tech Manufacturing Industries

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Naomi HERTZ
Keyword(s):  
Value Chain ◽  
Field Research ◽  
Sustainable Competitive Advantage ◽  
Manufacturing Plants ◽  
Footwear Industry ◽  
Technological Platform ◽  
Qualitative Methodologies ◽  
Developed World ◽  
Depth Interviews

Intensive manual labor enterprises in the developed world face challenges competing with products imported from countries where manufacturing costs are low. This reduces the volume of domestic production and leads to rapid loss of knowledge and experience in production processes. This study focuses on the Israeli footwear industry as a case study. Qualitative methodologies were applied, including in-depth interviews and field observations. A literature review on previous research, and contemporary trends was conducted. The field research examines challenges along the value chain in small factories. It finds that mass production paradigms impose a decentralized process between designers and manufacturers and therefore do not leverage local potential into a sustainable competitive advantage for small factories. The proposed solution is a digital and technological platform for small manufacturing plants. The platform mediates and designs the connections between production, technology, and design and enables the creation of a joint R&D system.

DESIGNING FOR AUTISM: AN ASPECTSS™ POST-OCCUPANCY EVALUATION OF LEARNING ENVIRONMENTS

2018 ◽  
Vol 12 (3) ◽  
pp. 308 ◽  
Author(s):  
Magda Mostafa
Keyword(s):  
Learning Environments ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Built Environments ◽  
Outdoor Learning ◽  
Learning Spaces ◽  
Post Occupancy Evaluation ◽  
8Th Grade ◽  
Evaluation Of Learning

The objective of this paper is to demonstrate the application of the Autism ASPECTSS™ Design Index in the Post-Occupancy Evaluation of existing learning environments for children along the autism spectrum. First published in 2014 this index outlines 7 design criteria that have been hypothesized to support environments conducive of learning for children with autism spectrum disorder (ASD). Using the index as a framework, this paper outlines a case study of a Post-Occupancy Evaluation (POE) of an existing pre-K-8th grade public charter purpose-built school for children on the autism spectrum. The tools used for the evaluation were: the ASPECTSS scoring of the school through a survey of teachers and administrators; on-site behavioral in-class observation; and focus groups of parents, teachers, staff and administrators. The results informed a design retro-fit proposal that strived to assess any ASPECTSS compliance issues and implement the index across the learning spaces, therapy spaces, support services and outdoor learning environments of the school. This paper will outline the application of the index and the resultant design from this process. The results will strive to present a scalable and replicable methodology and prototype for improving existing built environments for learners with ASD.

Scales and Units

2018 ◽  
Author(s):  
Kathryn M. de Luna
Keyword(s):  
Language Change ◽  
Historical Linguistics ◽  
Bantu Languages ◽  
The North ◽  
First Case ◽  
Linguistic Evidence ◽  
River Region ◽  
History Of ◽  
Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sener Dikmese ◽  
Kishor Lamichhane ◽  
Markku Renfors
Keyword(s):  
Spectrum Sensing ◽  
Filter Bank ◽  
Cooperative Spectrum Sensing ◽  
Minimum Energy ◽  
Cognitive Radios ◽  
Spectrum Management ◽  
Wireless Channel ◽  
First Case ◽  
Channel Effects

AbstractCognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

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