A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy

A configurable platform for synthetic chemistry incorporating an in-line benchtop NMR that is capable of monitoring and controlling organic reactions in real-time is presented. The platform is controlled via a modular LabView software control system for the hardware, NMR, data analysis and feedback optimization. Using this platform we report the real-time advanced structural characterization of reaction mixtures, including 19F, 13C, DEPT, 2D NMR spectroscopy (COSY, HSQC and 19F-COSY) for the first time. Finally, the potential of this technique is demonstrated through the optimization of a catalytic organic reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereoselectivity, multi-nuclear measurements or 2D correlations

On the fly multi-modal observation of ligand synthesis and complexation of Cu complexes in flow with ‘benchtop’ NMR and mass spectrometry

Exploring complex chemical systems requires reproducible and controllable ways to access non-equilibrium conditions. Herein we present a programmable flow system that can do both ligand synthesis and complexation on the fly, and the conditions of the reaction can be monitored using two simultaneous techniques, namely NMR and mass spectrometry. By using this approach we monitored the formation of unknown complexes, followed by crystallization that resulted in the characterisation of their structures giving 5 new compounds (4 isolated and fully characterised) which can be formulated as: Cu2(L1)4(μ-CO3)](BF4)2 (2); [Cu3(L1)6(μ-CO3)](PF6)2(OH)2 (3) [Cu2(L2)2](BF4)2 (4) and [Cu(L2)2](BF4)2·CH3CN (5)

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modest-size, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative ground-truth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work

Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture

Dual and broadband power dividers at microwave frequencies based on composite right/left handed (CRLH) lattice networks

CIMITECThis paper proposes a dual-band power divider operating at GHz frequencies and implemented by means of impedance transformers (also called inverters) based on lattice networks and transmission line sections. The dual-band functionality of the proposed device is achieved thanks to the composite right/left handed (CRLH) behavior of the impedance transformers, able to provide −90° and +90° phase shift at the first and second design frequencies, respectively, of the divider. By using such combination of transmission line sections and lattice networks, the characteristic impedance of the impedance transformers is roughly constant over wide bandwidths, with the results of broad operating bands. To demonstrate the possibilities of the approach, a prototype device is designed, fabricated and characterized

Barriers and Desired Affordances of Social Media Based e-Participation

The high rate of adoption of Social Media technologies and platforms make them naturally appealing for engaging citizens. Interestingly, despite the proliferation of e-Participation platforms, overall efforts towards mainstreaming Social Media-based and citizen-led political deliberations are still limited. Consequently, there is a paucity of research on effectiveness of Social Media technologies as e-Participation platforms; barriers to their use for e-Participation and their potentials to reshape deliberations on traditional e-Participation platforms. This paper investigates the perceived barriers to e-Participation and affordances of Social Media from the perspectives of senior decision maker and political actors. Grounded in the analytical framework for the duality e-Participation, we designed an instrument and interviewed 10 politicians and decision makers at different levels of government across three countries in Europe. Our results provide insights into barriers and perceived affordances of Social Media for e-Participation as well as the necessary conditions for increased adoption of Social Media for citizen-led participation

VR-Participation: On the feasibility of next-gen Virtual Reality technologies as Participation channel

While progress in the development of e-Participation platforms has been significant and the emergence of new Social Media-driven platforms appears to bring significant (by quantity), citizen engagement, little attention has been paid by researchers to the limitations of the pervasive textual communication for political participation. In this paper, we describe the major sociotechnical challenges of classic e-Participation solutions and how the emerging next-gen Virtual Reality (VR) technologies can be leveraged to alleviate some of the issues identified

A Semantic Deliberation Model for e-Participation

There have been very few attempts so far to develop a comprehensive and rigorous conceptualization for deliberations in e-participation. Without a rigorous and formal conceptualization of deliberation, consistent content descriptions creation, deliberation records sharing and seamless exploration is difficult. In addition, no e-participation deliberation ontology exists to support citizen-led e-participation particularly when considering contributions made on the social media platforms. This work bridges this gap by providing a rich conceptualization and corresponding formal and executable ontology for deliberation in the context of e-participation. The semantic model covers the core concepts of technology-mediated political discussion and explicitly supports the integrated citizen- and government-led model of e-Participation enabled by social media. Results from the use of the ontology in describing e-Participation deliberation information at Local Government projects are also presented

Structuring e-Participation Perspectives – Mapping and Aligning Models to Core Facets

A proliferation of e-participation research in recent years has produced fragmented contributions in the area of e-participation models. Without a mechanism for analyzing, relating and consolidating these models, further development of the domain is in danger of repeating itself. This paper presents such a mechanism – an Integrative Framework which organizes e-participation models based on the nature and specific aspects of e-participation supported. The Integrative Framework enables mapping of models to 12 different facets constructed from a combination of three related perspectives and four canonical aspects of e-participation. While our genealogical analysis of the models showed in general weak relationships among models, our Framework enabled logical groupings of these models as a basis for consolidation, alignment or complementarity analyses. Mappings also clearly revealed aspects of e-participation that are yet to be (sufficiently) addressed. We conclude with recommendations for fostering rigorous and incremental model development in the e-participation domain

VR-participation: The feasibility of the virtual reality-driven multi-modal communication technology facilitating e-Participation

Successful communication between citizens and decision makers – eParticipation, despite progressing from dedicated solutions to modern, social media-based approaches has been facing many challenges. We argue that Virtual Reality technologies through its sense of presence and embodiment for discussion participants can help in alleviating some of the major obstacles hindering effective communication and collaboration. In this paper, we propose a novel approach to building AI models to support effective dialog implementation in VR. VR platforms potentially afford studies on user behavior without the overhead of complicated sensor infrastructure required for data collection. In particular, we propose machine-learning-based approach for predictive log analytics to identify behavioral patterns that support or obstruct effective collaboration in the context of structured dialog conversation. We discuss the applicability of the models to e-Participation and possible broader application of the models created. We also argue that VR-interaction-data-based models have the potentials to be transferable to managing and improving real-life interactions