E-Jurnal Universitas Tunas Husada Tasikmalaya
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Speech emotion recognition using data augmentation method by cycle-generative adversarial networks
No full textOne of the obstacles in developing speech emotion recognition (SER) systems is the data scarcity problem, i.e., the lack of labeled data for training these systems. Data augmentation is an effective method for increasing the amount of training data. In this paper, we propose a cycle-generative adversarial network (cycle-GAN) for data augmentation in the SER systems. For each of the five emotions considered, an adversarial network is designed to generate data that have a similar distribution to the main data in that class but have a different distribution to those of other classes. These networks are trained in an adversarial way to produce feature vectors similar to those in the training set, which are then added to the original training sets. Instead of using the common cross-entropy loss to train cycle-GANs, we use the Wasserstein divergence to mitigate the gradient vanishing problem and to generate high-quality samples. The proposed network has been applied to SER using the EMO-DB dataset. The quality of the generated data is evaluated using two classifiers based on support vector machine and deep neural network. The results showed that the recognition accuracy in unweighted average recall was about 83.33%, which is better than the baseline methods compared
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
No full textIn recent years, malware detection has become an active research topic in thearea of Internet of Things (IoT) security. The principle is to exploitknowledge from large quantities of continuously generated malware. Existingalgorithms practice available malware features for IoT devices and lackreal-time prediction behaviors. More research is thus required on malwaredetection to cope with real-time misclassification of the input IoT data.Motivated by this, in this paper we propose an adversarial self-supervisedarchitecture for detecting malware in IoT networks, SETTI, considering samplesof IoT network traffic that may not be labeled. In the SETTI architecture, wedesign three self-supervised attack techniques, namely Self-MDS, GSelf-MDS andASelf-MDS. The Self-MDS method considers the IoT input data and the adversarialsample generation in real-time. The GSelf-MDS builds a generative adversarialnetwork model to generate adversarial samples in the self-supervised structure.Finally, ASelf-MDS utilizes three well-known perturbation sample techniques todevelop adversarial malware and inject it over the self-supervisedarchitecture. Also, we apply a defence method to mitigate these attacks, namelyadversarial self-supervised training to protect the malware detectionarchitecture against injecting the malicious samples. To validate the attackand defence algorithms, we conduct experiments on two recent IoT datasets:IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, theSelf-MDS method has the most damaging consequences from the attacker's point ofview by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, theASelf-MDS method is the most devastating algorithm that can plunge the accuracyrate from 98% to 77%
Spatial Structures; Movers and Shakers, Volume 3, Issue 1
No full textThe Spatial Structures; Movers and Shakers e-magazine was originally launchedin the build-up to the Spatial Structures 2020/21 conference, which wasorganised by the Spatial Structures Research Centre at the University of Surrey,and held in August 2021. Following the success of the conference, new editionsof the e-magazine are now published twice a year.Drawing from – and building on – the conference’s theme of ‘inspiring the next generation’, thise-magazine aims to reach out and encourage young people to enter the field of spatial structures,and to highlight and promote the exciting research and innovation taking place within the discipline.With this in mind, Spatial Structures; Movers & Shakers celebrates the life, work and achievementsof world-leading individuals who are involved in spatial structures, as well as spotlighting renownedorganisations and interesting projects that are pushing the boundaries within the field. We commendoutstanding contributions to research and education, as well as exploring new insights in design,fabrication and construction. The articles include Q&As based on video interviews which areavailable on the YouTube channel ‘SpatialStructures2021’. In addition, the video section ‘Your Space,Your Structure’ offers individuals the opportunity to present some of their own work and theirinspirations in the field of spatial structures.This e-magazine is published by the Spatial Structures Research Centre at the University of Surrey.We hope you enjoy reading it
Type Ia supernova ejecta-donor interaction: explosion model comparison
No full textIn the single-degenerate scenario of Type Ia supernovae (SNe Ia), the interaction between high-speed ejected material and the donor star in a binary system is expected to lead to mass being stripped from the donor. A series of multi-dimensional hydrodynamical simulations of ejecta-donor interaction have been performed in previous studies most of which adopt either a simplified analytical model or the W7 model to represent a normal SN Ia explosion. Whether different explosion mechanisms can significantly affect the results of ejecta-donor interaction is still unclear. In this work, we simulate hydrodynamical ejecta interactions with a main-sequence (MS) donor star in two dimensions for two near-Chandrasekhar-mass explosion models of SNe Ia, the W7 and N100 models. We find that about 0.30 and 0.37 M of hydrogen-rich material are stripped from a 2.5 M donor star in a 2 day orbit by the SN Ia explosion in simulations with the W7 deflagration and N100 delayed-detonation explosion model, respectively. The donor star receives a kick of about 74 and 86 km s −1 , respectively, in each case. The modal velocity, about 500 km s −1 , of stripped hydrogen-rich material in the N100 model is faster than the W7 model, with modal velocity of about 350 km s −1 , by a factor 1.4. Based on our results, we conclude that the choice of near-Chandrasekhar-mass explosion model for normal SNe Ia seems to not significantly alter the ejecta-donor interaction for a given main-sequence donor model, at least in 2D
Isoindolinium Groups as Stable Anion Conductors for Anion-Exchange Membrane Fuel Cells and Electrolyzers
No full textAnion-exchange membrane (AEM) fuel cells (AEMFCs) and water electrolyzers (AEMWEs) have gained strongattention of the scientific community as an alternative to expensive mainstream fuel cell and electrolysis technologies. However, in the high pH environment of the AEMFCs and AEMWEs, especially at low hydration levels, the molecular structure of most anion-conducting polymers breaks down because of the strong reactivity of the hydroxide anions with the quaternary ammonium (QA) cation functional groups that are commonly used in the AEMs and ionomers. Therefore, new highly stable QAs are needed to withstand the strong alkaline environment of these electrochemical devices. In this study, a series of isoindolinium salts with different substituents is prepared and investigated for their stability under dry alkaline conditions. We show that by modifying isoindolinium salts, steric effects could be added to change the degradation kinetics and impart significant improvement in the alkaline stability, reaching an order of magnitude improvement when all the aromatic positions are substituted. Density functional theory (DFT) calculations are provided in support of the high kinetic stability found in these substituted isoindolinium salts. This is the first time that this class of QAs has been investigated. We believe that these novel isoindolinium groups can be a good alternative in the chemical design of AEMs to overcome material stability challenges in advanced electrochemical systems.</p
Ni-Phosphide catalysts as versatile systems for gas-phase CO2 conversion: Impact of the support and evidences of structure-sensitivity
No full textWe report for the first time the support dependent activity and selectivity of Ni-rich nickel phosphide catalysts for CO2 hydrogenation. New catalysts for CO2 hydrogenation are needed to commercialise the reverse water–gas shift reaction (RWGS) which can feed captured carbon as feedstock for traditionally fossil fuel-based processes, as well as to develop flexible power-to-gas schemes that can synthesise chemicals on demand using surplus renewable energy and captured CO2. Here we show that Ni2P/SiO2 is a highly selective catalyst for RWGS, producing over 80% CO in the full temperature range of 350–750 °C. This indicates a high degree of suppression of the methanation reaction by phosphide formation, as Ni catalysts are known for their high methanation activity. This is shown to not simply be a site blocking effect, but to arise from the formation of a new more active site for RWGS. When supported on Al2O3 or CeAl, the dominant phase of as synthesized catalysts is Ni12P5. These Ni12P5 catalysts behave very differently compared to Ni2P/SiO2, and show activity for methanation at low temperatures with a switchover to RWGS at higher temperatures (reaching or approaching thermodynamic equilibrium behaviour). This switchable activity is interesting for applications where flexibility in distributed chemicals production from captured CO2 can be desirable. Both Ni12P5/Al2O3 and Ni12P5/CeAl show excellent stability over 100 h on stream, where they switch between methanation and RWGS reactions at 50–70% conversion. Catalysts are characterized before and after reactions via X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), temperature-programmed reduction and oxidation (TPR, TPO), Transmission Electron Microscopy (TEM), and BET surface area measurement. After reaction, Ni2P/SiO2 shows the emergence of a crystalline Ni12P5 phase while Ni12P5/Al2O3 and Ni12P5/CeAl both show the crystalline Ni3P phase. While stable activity of the latter catalysts is demonstrated via extended testing, this Ni enrichment in all phosphide catalysts shows the dynamic nature of the catalysts during operation. Moreover, it demonstrates that both the support and the phosphide phase play a key role in determining selectivity towards CO or CH4
Stochastic optimization for stationkeeping of periodic orbits using a high-order Target Point Approach
No full textPeriodic orbits in the Restricted Three-Body Problem are widely adopted as nominal trajectories by di↵erent missions. To maintain periodic orbits in a three-body regime, a stationkeeping strategy based on a high-order Target Point Approach (TPA) is proposed, where fuel-optimal and error-robust TPA parameters are acquired from stochastic global optimization. Accurate TPA maneuvers are calculated in a high-order fashion enabled by Di↵erential Algebra techniques. Orbit determination epoch is selected using a sensitivity analysis based on the convergence radius of a stroboscopic map. Stochasticity is handled by incorporating Monte Carlo simulations in the process of optimization and the evaluation of high-order ODE expansions is employed to supplant the time-consuming numerical integration. Two specific types of periodic orbits, Near Rectilinear Halo Orbits and Quasi-Satellite Orbits, are investigated to demonstrate the validity and eciency of the strategy
Last chance for wildlife: making tourism count for conservation
No full textNature-based tourism offers the opportunity for tourists to see first-hand both wildlife and the conservation efforts of organisations and individuals to protect habitats and species. Whilst recent studies hint that tourism can prompt visitors to provide philanthropic support for conservation, studies to-date have focused on behavioural intentions within specific case studies rather than actual behaviour, thereby limiting generalisability and explanatory scope. Consequently, little is known if and why individuals donate more after nature-based tourism. An online questionnaire, which included both quantitative and qualitive measures, explored key predictors of what triggers tourists to engage in philanthropic behaviour. Through a collaboration with two leading UK adventure travel companies, 924 participants' travel patterns and donation histories were examined to assess the role tourism plays in prompting new donations. Findings confirm, first, that travel to last chance destinations prompts higher instances of new philanthropy compared to other international and domestic trips; second, that other key factors, including the importance of stronger identity with nature and/or first-time visitation, influence new philanthropic support. Alongside the scholarly contributions, this study provides actionable guidance on how to encourage philanthropic behaviour working with both tour-operators and non-profit organisations
Global Sensitivity Analysis for a Perfusion Bioreactor based on CFD Modelling
No full text•A review and comparison of mathematical models for bioreactorsDevelopment and implementation of a mathematical model for a perfusion bioreactor•Global sensitivity analysis is performed, and the results are analyzed for two different scenarios•A relative gain analysis is performed and interpretedPerfusion bioreactors are important tools in tissue engineering that are used for cell cultivation. Unfortunately these types of processes are not yet fully understood in literature and information about the model is scarce. Furthermore, mathematical models that are used for perfusion bioreactors have posed significant challenges. This work presents a concise overview and analysis of mathematical models for a perfusion bioreactor process. The comprehensive mathematical model of convection and diffusion in a perfusion bioreactor, combined with cell growth kinetics, is developed using Computational Fluid Dynamics. The model describes the spatio-temporal evolution of glucose concentration, oxygen concentration, lactate concentration and cell density within a polymeric scaffold. For an in-depth understanding of this type of processes, global sensitivity analysis and simulations is performed using the method of high-dimensional model representation (RS-HDMR). A quantitative analysis of the complex kinetic mechanisms using recently developed advanced mathematical approaches to global sensitivity and uncertainty analysis through RS-HDMR can be exploited to investigate the important features of the perfusion bioreactor process as well as possible factors underlying qualitative discrepancies. Moreover, for a further understanding of the process, a relative gain analysis is performed. The results will help us gain an in depth understanding of the process and will be used as the foundation for advanced control algorithms that will facilitate manufacturing for any type of cell culture using a continuous perfusion bioreactor thus paving the way towards Industry 4.0
Changing healthcare professionals' non-reflective processes to improve the quality of care
No full textTranslating research evidence into clinical practice to improve care involves healthcare professionals adopting new behaviours and changing or stopping their existing behaviours. However, changing healthcare professional behaviour can be difficult, particularly when it involves changing repetitive, ingrained ways of providing care. There is an increasing focus on understanding healthcare professional behaviour in terms of non-reflective processes, such as habits and routines, in addition to the more often studied deliberative processes. Theories of habit and routine provide two complementary lenses for understanding healthcare professional behaviour, although to date, each perspective has only been applied in isolation.To combine theories of habit and routine to generate a broader understanding of healthcare professional behaviour and how it might be changed.Sixteen experts met for a two-day multidisciplinary workshop on how to advance implementation science by developing greater understanding of non-reflective processes.From a psychological perspective ‘habit’ is understood as a process that maintains ingrained behaviour through a learned link between contextual cues and behaviours that have become associated with those cues. Theories of habit are useful for understanding the individual's role in developing and maintaining specific ways of working. Theories of routine add to this perspective by describing how clinical practices are formed, adapted, reinforced and discontinued in and through interactions with colleagues, systems and organisational procedures. We suggest a selection of theory-based strategies to advance understanding of healthcare professionals' habits and routines and how to change them.Combining theories of habit and routines has the potential to advance implementation science by providing a fuller understanding of the range of factors, operating at multiple levels of analysis, which can impact on the behaviours of healthcare professionals, and so quality of care provision.•Improving the quality of care involves changing healthcare professional behaviour.•Professional behaviour is driven by both reflective and non-reflective processes.•Changing non-reflective, habitual, or routine clinical behaviours is difficult.•Theory-based strategies can help address non-reflective clinical behaviours.•Future directions for research on non-reflective clinical behaviour are provided