Effectiveness of an Interactive Application to Assist Learning: A Test Case

Advances in computer technologies have made it possible to develop computer-aided learning tools for enhanced learning. Today, most researchers in the field of educational technology seem to be preoccupied with either the development of Artificial Intelligence applications or the representation of various learning theories such as constructivism by a computer program. The enthusiasm to develop technologically advanced learning tools resulted in technologies with limited application. The need to develop simple computer-based tools to assist instruction and demonstrate its effectiveness to enhance learning is paramount. Moreover, those tools need to be designed and integrated into a pedagogical framework. As a result, the instructor transforms into a content facilitator with altered needs. This paper presents the design and use of an interactive computer-aided learning tool for enhanced learning. Two participant groups were randomly selected. One group was allowed to use the interactive computer-aided tool prior to a test, while the second group was not. Performance of the groups was compared. Results revealed a higher mean test score for group one. The impact of the tool on test scores was found to be significant. The findings have direct implications on the design, development, testing and implementation of interactive computer-aided learning tools and on today\u27s transforming roles of educators and learners

Excited State-Specific CASSCF Theory for the Torsion of Ethylene

State-specific complete active space self-consistent field (SS-CASSCF) theory has emerged as a promising route to accurately predict electronically excited energy surfaces away from molecular equilibria. However, its accuracy and practicality for chemical systems of photochemical interest have yet to be fully determined. We investigate the performance of the SS-CASSCF theory for the low-lying ground and excited states in the double bond rotation of ethylene. We show that state-specific approximations with a minimal (2e,2o) active space provide comparable accuracy to state-averaged calculations with much larger active spaces, while optimizing the orbitals for each excited state significantly improves the spatial diffusivity of the wave function. However, the incorrect ordering of state-specific solutions causes excited state solutions to coalesce and disappear, creating unphysical discontinuities in the potential energy surface. Our findings highlight the theoretical challenges that must be overcome to realize practical applications of state-specific electronic structure theory for computational photochemistry

Excited state-specific CASSCF theory for the torsion of ethylene

State-specific complete active space self-consistent field (SS-CASSCF) theory has emerged as a promising route to accurately predict electronically excited energy surfaces away from molecular equilibria. However, its accuracy and practicality for chemical systems of photochemical interest has yet to be fully determined. We investigate the performance of SS-CASSCF theory for the low-lying ground and excited states in the double bond rotation of ethylene. We show that state-specific approximations with a minimal (2e, 2o) active space provide comparable accuracy to state-averaged calculations with much larger active spaces, while optimising the orbitals for each excited state significantly improves the spatial diffusivity of the wave function. However, the unbalanced post-CASSCF dynamic correlation in valence and Rydberg excitations, or the use of a non-diffuse basis set, causes excited state solutions to coalesce and disappear, creating unphysical discontinuities in the potential energy surface. Our findings highlight the theoretical challenges that must be overcome to realise practical applications of state-specific electronic structure theory for computational photochemistry.Comment: 10 pages, 6 figure

Immersive Authoring of 360 Degree Interactive Applications

Although there are proposals in the literature for authoring mulsemedia (mul tiple se nsorial media) applications with 2D content, there are no suitable solutions when it comes to 360° content. Moreover, little consensus on 360° mulsemedia authoring methodology exists. Aiming at filling this gap, we propose the concept of immersive authoring of 360° multisensory applications. Our proposal comprises an immersive 360° authoring environment to bring the author closer to the final user presentation environment. We implemented our proposal in AMUSEVR, a virtual-reality (VR) environment for authoring 360° mulsemedia applications. We see it as an alternative or a possible complement to available 2D mulsemedia authoring tools. AMUSEVR provides creation and editing of interactive multiple sensorial media scenes by directly arranging objects in a 3D space using VR technology. Also, the tool allows users to run their applications through AMUSEVR viewer mode. We used the Goal Question Metric (GQM) approach to plan our tests and a group of users evaluated the tool with the SUS and UEQ questionnaires, obtaining a SUS score of 82.25 and an excellent UEQ benchmark, which are very promising results.10.13039/501100002322-Brazilian National Council for Scientific and Technological Development (CNPq), Carlos Chagas Filho Foundation for Research Support in the State of Rio de Janeiro (FAPERJ), Coordination for the Improvement of Higher Education Personnel (CAPES), and Program for Institutional Internationalization (CAPES PrInt

Welcome to SensoryX 2023

We are excited to welcome you to the third edition of the SensoryX workshop on multisensory experiences. Building on the success of the previous two workshops (also co-located with IMX) in New York and Aveiro, the current workshop examines different aspects of multisensory design - from authoring tools to the evaluation of multisensory experiences - with the aim of identifying the current challenges and opportunities of mulsemedia

Preparation and Loading with Rifampicin of Sub-50 nm Poly(ethyl cyanoacrylate) Nanoparticles by Semicontinuous Heterophase Polymerization

We report the preparation of poly(ethyl cyanoacrylate) (PECA) nanoparticles by semicontinuous heterophase polymerization carried out at monomer starved conditions at three monomer addition rates. Particles in the nanometer range were obtained, the size of which diminishes with decreasing monomer addition rate as shown by the fact that particles with mean diameters of ca. 42 and 30 nm were obtained at the faster and intermediate dosing rates, respectively, whereas two populations of particles, one of 15.5 and the other of 36 nm in mean diameters, were produced at the slower dosing rate. The obtained molecular weights were from 2,200 to 3,500 g/mol, depending on the addition rate, which are typical of the anionic polymerizations of cyanoacrylates in aqueous dispersions at low pHs. The rifampicin (RIF) loading into the nanoparticles was successful since the entire drug added was incorporated. The drug release study carried out at pH of 7.2 indicated a faster release from the free RIF at intermediate and larger release times as expected since, in the nanoparticles, first the drug has to diffuse through the nanoparticle structure. The comparison of several drug release models indicates that the RIF release from PECA nanoparticles follows that of Higuchi

Assessing Usefulness, Ease of Use and Recognition Performance of Semi-Automatic Mulsemedia Authoring

Mulsemedia (Multiple Sensorial Media) authoring poses a considerable challenge as authors navigate the intricate task of identifying moments to activate sensory effects within multimedia content. A novel proposal is to integrate content recognition algorithms that use machine learning (ML) into authoring tools to alleviate the authoring effort. As author subjectivity is very important, it is imperative to allow users to define which sensory effects should be automatically extracted. This paper conducts a twofold evaluation of the proposed semi-automatic authoring. The first is from a user perspective within the STEVE 2.0 mulsemedia authoring tool, employing the Goal-Question-Metric (GQM) methodology and a user feedback questionnaire. Our user evaluation indicates that users perceive the semi-automatic authoring approach as a positive enhancement to the authoring process. The second evaluation targets sensory effect recognition using two different content recognition modules, quantifying their automatic recognition capabilities against manual authoring. Metrics such as precision, recall, and F1 scores provide insights into the strengths and nuances of each module. Differences in label assignments underscore the need for ML module result combination methodologies. These evaluations contribute to a comprehensive understanding of the effectiveness of sensory effect recognition modules in enhancing mulsemedia content authoring.The authors wish to thank CAPES, CAPES Print, CNPQ, INCT-MACC and FAPERJ for the partial financing of this work

Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation

Buildings are major sources of greenhouse gas (GHG) emissions and contributors to the climate crisis. To meet climate-change mitigation needs, one must go beyond operational energy consumption and related GHG emissions of buildings and address their full life cycle. This study investigates the global trends of GHG emissions arising across the life cycle of buildings by systematically compiling and analysing more than 650 life cycle assessment (LCA) case studies. The results, presented for different energy performance classes based on a final sample of 238 cases, show a clear reduction trend in life cycle GHG emissions due to improved operational energy performance. However, the analysis reveals an increase in relative and absolute contributions of so‐called ‘embodied’ GHG emissions, i.e., emissions arising from manufacturing and processing of building materials. While the average share of embodied GHG emissions from buildings following current energy performance regulations is approximately 20–25% of life cycle GHG emissions, this figure escalates to 45–50% for highly energy-efficient buildings and surpasses 90% in extreme cases. Furthermore, this study analyses GHG emissions at time of occurrence, highlighting the ‘carbon spike’ from building production. Relating the results to existing benchmarks for buildings’ GHG emissions in the Swiss SIA energy efficiency path shows that most cases exceed the target of 11.0 kgCO$^{2}$eq/m$^{2}$a. Considering global GHG reduction targets, these results emphasize the urgent need to reduce GHG emissions of buildings by optimizing both operational and embodied impacts. The analysis further confirmed a need for improving transparency and comparability of LCA studies

Embodied GHG emissions of buildings - Critical reflection of benchmark comparison and in-depth analysis of drivers

In the face of the unfolding climate crisis, the role and importance of reducing Greenhouse gas (GHG) emissions from the building sector is increasing. This study investigates the global trends of GHG emissions occurring across the life cycle of buildings by systematically compiling life cycle assessment (LCA) studies and analysing more than 650 building cases. Based on the data extracted from these LCA studies, the influence of features related to LCA methodology and building design is analysed. Results show that embodied GHG emissions, which mainly arise from manufacturing and processing of building materials, are dominating life cycle emissions of new, advanced buildings. Analysis of GHG emissions at the time of occurrence, shows the upfront \u27carbon spike\u27 and emphasises the need to address and reduce the GHG \u27investment\u27 for new buildings. Comparing the results with existing life cycle-related benchmarks, we find only a small number of cases meeting the benchmark. Critically reflecting on the benchmark comparison, an in-depth analysis reveals different reasons for cases achieving the benchmark. While one would expect that different building design strategies and material choices lead to high or low embodied GHG emissions, the results mainly correlate with decisions related to LCA methodology, i.e. the scope of the assessments. The results emphasize the strong need for transparency in the reporting of LCA studies as well as need for consistency when applying environmental benchmarks. Furthermore, the paper opens up the discussion on the potential of utilizing big data and machine learning for analysis and prediction of environmental performance of buildings

Semi-Automatic mulsemedia authoring analysis from the user's perspective

Mulsemedia (Multiple Sensorial Media) authoring is a complex task that requires the author to scan the media content to identify the moments to activate sensory effects. A novel proposal is to integrate content recognition algorithms into authoring tools to alleviate the authoring effort. Such algorithms could potentially replace the work of the human author when analyzing audiovisual content, by performing automatic extraction of sensory effects. Besides that, the semi-Automatic method proposes to maintain the author subjectivity, allowing the author to define which sensory effects should be automatically extracted. This paper presents an evaluation of the proposed semi-Automatic authoring considering the point of view of users. Experiments were done with the STEVE 2.0 mulsemedia authoring tool. Our work uses the GQM (Goal Question Metric) methodology, a questionnaire for collecting users' feedback, and analyzes the results. We conclude that users believe that the semi-Automatic authoring is a positive addition to the authoring method.CAPES, CAPES Print, CNPQ, INCT-MACC and FAPERJ