Electrolytic cell engineering and device optimization for electrosynthesis of e-biofuels via co-valorisation of bio-feedstocks and captured CO2

Utilizing CO₂ in an electro-chemical process and synthesizing value-added chemicals are amongst the few viable and scalable pathways in carbon capture and utilization technologies.CO₂ electro-reduction is also counted as one of the main options entailing less fossil fuel consumption and as a future electrical energy storage strategy.The current study aims at developing a new electrochemical platform to produce low-carbon e-biofuel through multifunctional electrosynthesis and integrated co-valorisation of biomass feedstocks with captured CO₂.In this approach,CO₂ is reduced at the cathode to produce drop-in fuels(e.g.,methanol)while value-added chemicals(e.g.,selective oxidation of alcohols,aldehydes,carboxylic acids and amines/amides)are produced at the anode.In this work,a numerical model of a continuous-flow design considering various anodic and cathodic reactions was built to determine the most techno-economically feasible configurations from the aspects of energy efficiency,environment impact and economical values.The reactor design was then optimized via parametric analysis.     

Design optimization of the quantization and a pipelined 2D-DCT for real-time applications

The Discrete Cosine Transform (DCT) is one of the most widely used techniques for image compression. Several algorithms are proposed to implement the DCT-2D. The scaled SDCT algorithm is an optimization of the DCT-1D, which consists in gathering all the multiplications at the end. In this paper, in addition to the hardware implementation on an FPGA, an extended optimization has been performed by merging the multiplications in the quantization block without having an impact on the image quality. A simplified quantization has been performed also to keep higher the performances of the all chain. Tests using MATLAB environment have shown that our proposed approach produces images with nearly the same quality of the ones obtained using the JPEG standard. FPGA-based implementations of this proposed approach is presented and compared to other state of the art techniques. The target is an an Altera Cyclone II FPGA using the Quartus synthesis tool. Results show that our approach outperforms the other ones in terms of processing-speed, used resources and power consumption. A comparison has been done between this architecture and a distributed arithmetic based architecture.     

Trust-based authentication scheme with user rating for low-resource devices in smart environments

In smart environments, pervasive computing contributes in improving daily life activities for dependent people by providing personalized services. Nevertheless, those environments do not guarantee a satisfactory level for protecting the user privacy and ensuring the trust between communicating entities. In this study, we propose a trust evaluation model based on user past and present behavior. This model is associated with a lightweight authentication key agreement protocol (Elliptic Curve-based Simple Authentication Key Agreement). The aim is to enable the communicating entities to establish a level of trust and then succeed in a mutual authentication using a scheme suitable for low-resource devices in smart environments. An innovation in our trust model is that it uses an accurate approach to calculate trust in different situations and includes a human-based feature for trust feedback, which is user rating. Finally, we tested and implemented our scheme on Android mobile phones in a smart environment dedicated for handicapped people.     

Analytics of learning tactics and strategies in an online learnersourcing environment

Background

The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.

Objectives

To contribute to filling this gap, this study explores how students engage with learnersourcing tasks across a range of course and assessment designs.

Methods

We conducted an exploratory study on trace data of 1279 students across three courses, originating from the use of a learnersourcing environment under different assessment designs. We employed a new methodology from the learning analytics (LA) field that aims to represent students' behaviour through two theoretically-derived latent constructs: learning tactics and the learning strategies built upon them.

Results

The study's results demonstrate students use different tactics and strategies, highlight the association of learnersourcing contexts with the identified learning tactics and strategies, indicate a significant association between the strategies and performance and contribute to the employed method's generalisability by applying it to a new context.

Implications

This study provides an example of how learning analytics methods can be employed towards the development of effective learnersourcing systems and, more broadly, technological educational solutions that support learner-centred and data-driven learning at scale. Findings should inform best practices for integrating learnersourcing activities into course design and shed light on the relevance of tactics and strategies to support teachers in making informed pedagogical decisions.     

Video transmission enhancement in presence of misbehaving nodes in MANETs

Mobile Ad-hoc NETworks (MANET) are infrastructureless networks where self-configuring mobile nodes are connected by wireless links. Because of its decentralized operation, these nodes rely on each other to store and forward packets. Video transmission over MANETs is more challenging than over conventional wireless networks due to rapid topology changes and lack of central administration. Most of the proposed MANET protocols assume that all nodes are working within a cooperative and friendly network context. However, misbehaving nodes that exhibit abnormal behaviors can disrupt the network operation and affect the network availability by refusing to cooperate to route packets due to their selfish or malicious behavior. In this paper, we examine the effect of packet dropping attacks on video transmission over MANETs. We also study the effects of mitigation using intrusion detection systems to MANET in presence of video traffic. To the best of our knowledge, this is the first attempt to study multimedia over such environments. We propose a novel intrusion detection system, which is an adaptive acknowledgment scheme (AACK) with the ability to detect misbehaved nodes and avoid them in other transmissions. The aim of AACK scheme is to overcome watchdog weaknesses due to collisions and limited transmission power and also to improve TWOACK scheme. To demonstrate the performance of our proposed scheme, simulation experiments are performed. The results of our experiments show that MPEG4 is more suitable for our simulation environment than H264 video traffic. The simulation results show that AACK scheme provides better network performance with less overhead than other schemes; it also shows that AACK outperforms both TWOACK and watchdog in video transmission applications in the presence of misbehaving nodes.     

Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

Structural Control of NiO–YSZ/LSCF–YSZ Dual‐Layer Hollow Fiber Membrane for Potential Syngas Production

The objective of this study was to fabricate dual‐layer hollow fiber as a microreactor for potential syngas production via phase inversion‐based co‐extrusion/cosintering process. As the main challenge of phase inversion is the difficulty to obtain defect‐free fiber, this work focuses on the effect of the fabrication parameters, that is, nonsolvent content, sintering temperature and outer‐layer extrusion rate, on the macrostructure of the produced hollow fiber. SEM images confirm that the addition of nonsolvent has successfully minimized the finger‐like formation. At high sintering temperature, more dense hollow fiber was formed while outer‐layer extrusion rate affects the outer layer thickness.