Enzyme-based degradation of recalcitrant organic pollutants is a promising and environmentally friendly remediation approach. Oxidoreductases, particularly, peroxidases are the most popular class of enzymes for bioremediation of contaminated water. Importantly, enzyme immobilization is becoming a powerful tool for the improvement of enzyme activity in different environments, stability, and reusability. Moreover, the proper selection of the host material is crucial for the enhancement of the enzyme properties. Here, we have explored covalent organic frameworks (COFs) as a porous support material for the immobilization of horseradish peroxidase (HRP) and tested its efficiency against the degradation of toxic dyes in water. Our findings suggest increased stability of the immobilized enzyme while maintaining the catalytic efficiency, in turns, pave the way for effective reusability. The preliminary results of the degradation studies provide the plausible use of COF-immobilized enzymes in remediation applications.
In the current study, cobalt ferrite supported on reduced graphene oxide is synthesized via a hydrothermal method and characterized using the FT-IR, XRD, SEM, TEM, and BET. Then investigated its catalytic performance for phenol degradation in water under various experimental conditions and reactors. Cobalt ferrite nanoparticles are of interest due to their magnetic nature, anti-microbial activity, controllable small size, and monitored by external magnetic field.
Petroleum refining has been one of the key technologies driving global economic development and technological advancement for well over a century. Although much of the technology used in refineries is considered mature, the industry is always seeking ways to make process improvements, reduce environmental impact, enhance safety, and achieve cost reductions. In particular, much focus has been placed on improving the existing technology for desulfurization in terms of efficiency and energy demand. The aim of this work is to investigate the techno-economic feasibility of implementing ionic liquid-based desulfurization on an industrial scale in oil refineries to complement or replace existing HDS. Several process configurations will be conceptualized and compared using thermodynamic and process simulation models. In particular, the challenge of ionic liquid regeneration, which has largely been ignored in literature, will be addressed and several potential regeneration methods will be compared using simulation tools.
The development of effective and low-cost catalysts for the hydrogenation and stabilization of bio-oils is still a challenge to overcome. Several nanostructured tungsten trioxide (WO3) catalysts were synthesized and characterized in this study to investigate their catalytic activity and selectivity towards hydrogenation of furfural to useful products such as furfuryl alcohol. The morphology of the catalysts was tuned via surfactant-assisted hydrothermal and precipitation processes. Three samples were synthesized from each method with the variation of the surfactant type, cationic dodecyl dimethyl ammonium bromide, and anionic poly (ethylene-alt-maleic anhydride), to direct the structure formation. D-WO3 catalyst prepared by both methods was the most effective catalyst in terms of conversion and selectivity.
The unpredictably increasing demand of volumetric video streaming for capturing immersive objects has enabled today's multimedia research community to focus on video streaming in six-degree-of-freedom (6DoF). However, there is no one-size-fits-all solution for an end-to-end (from capturing to rendering) volumetric 6DoF video streaming over HTTP. Therefore, this paper presents an architectural approach for an end-to-end 6DoF video streaming, leveraging the de-facto HTTP-based streaming protocol over Internet (a.k.a Dynamic Adaptive Streaming Over HTTP; DASH). The envision architecture depicts and realizes an end-to-end video streaming architecture from 6DoF media capturing to its rendering at the client-side. The proposed architecture complies with existing Internet architecture and consists of three essential components such as: 1) Video Provisioning, 2) Delivery, and 3) Consumption. The architecture exhibits future optimizations to the researchers and practitioners of volumetric video delivery.
Recently, the research community of HTTP-based video delivery (known as HTTP Adaptive streaming; HAS) has proposed many adaptive bitrate (ABR) techniques for dynamically selecting suitable bitrates and adapting to the available network conditions. However, it is imperative to understand the working principle of these ABR techniques. In this paper, we investigated client-driven heuristic-based ABR techniques that use buffer level (a.k.a buffer-based) or estimated throughput (a.k.a throughput-based) to select bitrate for video-on-demand delivery services. To do so, we used the open-source dash.js reference player which implements these techniques. For performance evaluation, the experiments are conducted using various video sessions and under different network conditions, in which we have collected buffer level, selected bitrate level, and latency for each video session. Our result analysis reveals that the throughput-based technique outperforms the buffer-based technique when the target is to achieve the highest video bitrate with fewer rebuffering events.
Nowadays, users and large corporations are looking for strong deep learning models that requires large amount of data; however, they are reluctant to share their private data. Federated learning (FL) enables decentralized training on decentralized data. Without the need to share the data, users can train machine learning models locally, and share the model instead. The main issue with the server-based federated learning is the centralization of governance, the server has a full control on the federated learning process. To solve this issue, a blockchain-based federated learning system is proposed, the blockchain will enable trust in a fully decentralized federated learning environment.
Android is the most widespread smartphone operating system. Its popularity attracted attackers to develop all sorts of malicious applications. On the defense side, much research has been done toward identifying Android applications type and state based on their system-level behavior. This paper describes a Machine Learning technique to detect whether an application is currently running in the foreground or not. The technique is aimed at boosting the accuracy of behavioral malware detection, by identifying apps performing suspect I/O activities while running in the background. We report that a lightly trained ML model focusing on a limited number of system calls over several applications is able to identify the running application along with its state with high confidence.
This paper presents a novel hybrid CMOS-Memristor-based in-memory processing architecture of the Random Spray Retinex algorithm for Imaging applications. The presented approach supports parallel computing and provides efficiency gains in area and power compared to traditional implementation. It uses the analog computations in the memristor crossbar and exploits the same physical elements for both processing and storage. As a result, it substantially reduces the computational complexity resulting from the data-intensive algorithm. Random Spray Retinex is presented as a case study. The experiments suggest that the parallel memristor-based in-memory computation is highly efficient in achieving a high resolution while greatly accelerating the RSR algorithm. The proposed approach is considered a milestone towards low-complexity and real-time Hardware architecture and design of the retinex algorithm.
More content providers use HTTP Adaptive Streaming have started delivering high quality streams with advanced end-to-end encryption mechanisms. This increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Accordingly, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services which mostly rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality. Our solution uses Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.
