Toluene degradation by a water/silicone oil mixture for the design of two phase partitioning bioreactors

Toluene degradation performances were studied in a 10 L Two-Phase Partitioning Bioreactor(TPPB).The liquid phase consisted of a mixture of water and PDMS 50(Poly Di Methyl Siloxane,i.e.silicone oil,viscosity of 46 m Pa·s) in the volume ratio of 75%/25%.Two series of experiments were carried out:in the first,the reactor was sequentially supplied with toluene whereas in the second,toluene was continuously supplied.Activated sludge from the wastewater treatment plant of Beaurade(Rennes,France) was used at an initial concentration of 0.5 dry mass g·(mixture L)^(-1).The elimination capacity(EC) was investigated as well as the change in biomass concentration over time.Toluene biodegradation was very ef ficient(removal ef ficiency,RE=100%) for toluene flows ranging from 0.2 to 1.2 ml·h^(-1),corresponding to elimination capacities of up to 104 g·m^(-3)·h^(-1).For a toluene flow of 1.2 ml·h^(-1),the biomass concentration measured at the end of the experiment was 4.7 dry mass g·(mixture L)^(-1).The oxygen concentration in the liquid phase was clearly not a limiting factor in these operating conditions.Based on these results,an extrapolation leading to the design of a large-scale pilot TPPB can now be considered to study toluene degradation performances in industrial conditions.

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks.

Shared Resource Quality Monitoring and Dynamic Trust Management in a Community Cloud

The collaboration tools offered by Cloud Computing have increased the need to share data and services within companies or between autonomous organizations. This has led to the deployment of community cloud infrastructures. However, several challenges will arise from this grouping of heterogeneous organizations. One of the main challenges is the management of trust between the actors of the community. Trust issues arise from the uncertainty about the quality of the resources and entities involved. The quality of a resource can be examined from a security or functional perspective. Therefore, ensuring security and monitoring the quality of resources is to ensure a high level of trust. Therefore, we propose in this paper a technique for dynamic trust management and quality monitoring of resources shared between organizations. Our approach consists, on the one hand, in evaluating the quality of resources based on quality of service measurement attributes and, on the other hand, in updating the trust values according to the information deduced from these measurements. The proposed framework is evaluated in terms of resource sharing success rate and execution time. Experimental results and comparison with TNA-SL and InterTrust models show that the framework can identify and track the behavior of malicious organizations with relatively low execution time.

Trust Assessment Model Based on a Zero Trust Strategy in a Community Cloud Environment

The adoption of Cloud Computing services in everyday business life has grown rapidly in recent years due to the many benefits of this paradigm. The various collaboration tools offered by Cloud Computing have eliminated or reduced the notion of distance between entities of the same company or between different organizations. This has led to an increase in the need to share resources (data and services). Community Cloud environments have thus emerged to facilitate interactions between organizations with identical needs and with specific and high security requirements. However, establishing trust and secure resource sharing relationships is a major challenge in this type of complex and heterogeneous environment. This paper proposes a trust assessment model (SeComTrust) based on the Zero Trust cybersecurity strategy. First, the paper introduces a community cloud architecture subdivided into different security domains. Second, it presents a process for selecting a trusted organization for an exchange based on direct or recommended trust value and reputation. Finally, a system for promoting or relegating organizations in the different security domains is applied. Experimental results show that our model guarantees the scalability of a community cloud with a high success rate of secure and quality resource sharing.

Anaerobic Digestion of Liquid Waste from an AttiékéFactory: From the Experimental Scale to the Semi-Industrial Scale

This study focused on the transfer of experimental results of anaerobic digestion of liquid waste from an attiéké (steamed cassava semolina) factory to a 6 m3 pilot digester. The experimental digester and the pilot were powered as follows: Lw + U + C (liquid waste + urine + cow dung). To the results, the experimental digester mesophilic with a progressive elimination of COD. Also, the nitrogen concentrations in the experimental reactor had little removal with alkaline pH. As for the biogas product in this digester, a volume of 3.6 m3 was obtained with a positive flammability test. The transition from the laboratory scale to the semi-industrial scale retains the results of purification and fuel biogas production of the experimental digester.

Super-resolution computed tomography imaging spectrometry

Computed tomography imaging spectrometry(CTIS) is a snapshot spectral imaging technique that relies on a limited number of projections of the target data cube(2D spatial and 1D spectral), which can be reconstructed via a delicate tomographic reconstruction algorithm. However, the restricted angle difference between the projections and the space division multiplexing of the projections make the reconstruction suffer from severe artifacts as well as a low spatial resolution. In this paper, we demonstrate super-resolution computed tomography imaging spectrometry(SRCTIS) by assimilating the information obtained by a conventional CTIS system and a regular RGB camera, which has a higher pixel resolution. To improve the reconstruction accuracy of CTIS, the unique information provided by the zero-order diffraction of the target scene is used as a guidance image for filtering to better preserve the edges and reduce artifacts. The recovered multispectral image is then mapped onto the RGB image according to camera calibration. Finally, based on the spectral and the spatial continuities of the target scene, the multispectral information obtained from CTIS is propagated to each pixel of the RGB image to enhance its spectral resolution, resulting in SRCTIS. Both stimulative studies and proof-of-concept experiments were then conducted, and the results quantified by key metrics, such as structural similarity index measurement and spectral angle mapping have suggested that the developed method cannot only suppress the reconstruction artifacts, but also simultaneously achieve high spatial and spectral resolutions.