This study evaluated the removal of diclofenac (DCF) in activated sludge and its long-term exposure effects on the function and structure of the microbial community. Activated sludge could remove ...<50% of 50 μg/L DCF. The removal decreased significantly to below 15% when DCF concentrations increased to 500 and 5000 μg/L. Quantitative assessment of the fate of DCF showed that its main removal routes were biodegradation (21%) and adsorption (7%), with other abiotic removals being insignificant (<5%). The biodegradation occurred through cometabolic mechanisms. DCF exposure in the range of 50–5000 μg/L did not disrupt the major functions of the activated sludge ecosystem (e.g. biomass yield and heterotrophic activity) over two months of DCF exposure. Consistently, 16S rRNA gene-based community analysis revealed that the overall community diversity (e.g. species richness and diversity) and structure of activated sludge underwent no significant alterations. The analysis did uncover a significant increase in several genera, Nitratireductor, Asticcacaulis, and Pseudacidovorax, which gained competitive advantages under DCF exposure. The enrichment of Nitratireductor, Asticcacaulis, and Pseudacidovorax genus might contribute to DCF biodegradation and emerge as a potential microbial niche for the removal of DCF.
Display omitted
•Activated sludge can remove 10–50% of DCF by biotransformation and adsorption.•Activated sludge biological function was resilient to DCF exposure at 50–5000 μg/L.•Microbial community was not altered by DCF exposure at 50–5000 μg/L.•Nitratireductor, Asticcacaulis and Pseudacidovorax have potential to biotransform DCF.
Display omitted
•CM addition for direct interspecies electrons transfer can enhance AD performance.•Research to date is limited to carbon and iron based CMs using small scale reactors.•Future work at ...pilot scale is recommended to validate the benefits of CM addition.•Future work is also to access the impact of CM addition on downstream processing.
Addition of conductive materials (CMs) has been reported to facilitate direct interspecies electron transfer (DIET) and improved anaerobic digestion (AD) performance. This review summarises the benefits and outlines remaining research challenges of the addition of CMs with a focus on the downstream processing of AD. CM addition may alter biogas quality, digestate dewaterability, biosolids volume, and centrate quality. Better biogas quality has been observed due to the adsorption of H2S to metallic CMs. The addition of CMs results in an increase in solid content of the digestate and thus an additional requirement for sludge dewatering and handling and the final biosolids volume for disposal. This review highlights the need for more research at pilot scale to validate the benefits of CM addition and to evaluate CM selection, doses, material costs, and the impact on downstream processes. The lack of research on the impact of CMs on the downstream process of AD is highlighted.
The diversity of microalgae and bacteria allows them to form a complementary consortium for efficient wastewater treatment and nutrient recovery. This review highlights the potential of ...wastewater-derived microalgal biomass as a renewable feedstock for producing animal feed, biofertilisers, biofuel, and many valuable biochemicals. Data corroborated from this review shows that microalgae and bacteria can thrive in many environments. Microalgae are especially effective at utilising nutrients from the water as they grow. This review also consolidates the current understanding of microalgae characteristics and their interactions with bacteria in a consortium system. Recent studies on the performance of only microalgae and microalgae-bacteria wastewater treatment are compared and discussed to establish a research roadmap for practical implementation of the consortium systems for various wastewaters (domestic, industrial, agro-industrial, and landfill leachate wastewater). In comparison to the pure microalgae system, the consortium system has a higher removal efficiency of up to 15% and shorter treatment time. Additionally, this review addresses a variety of possibilities for biomass application after wastewater treatment.
Display omitted
•Microalgae are highly diverse thus can complement bacteria in a consortium system.•Recent examples show the potential of microalgae-bacteria for nutrient removal.•Efficacy of pure and consortium systems for wastewater treatment is compared.•Microalgae-bacteria consortium can be used for a range of wastewater.•Strategies to improve microalgae-bacteria consortium are discussed.
This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial ...vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.
We propose an unmanned aerial vehicle (UAV) communications scheme with spectrum-sharing mechanism to provide mission-critical services such as disaster recovery and public safety. Specifically, the ...UAVs can serve as flying base stations to provide extended network coverage for the affected area under spectrum-sharing cognitive radio networks (CRNs). To cope with the effects of network destruction in a disaster, we propose a real-time optimisation framework for resource allocation (e.g., power and number of UAVs) for CRNs assisted by UAV relays. The proposed optimisation scheme aims at optimising the network throughput of primary and secondary networks under the stringent constraint of maximum tolerable interference impinged on the primary users. We also propose a deep neural network (DNN) model to significantly reduce the execution time under real-time solution of mixed-integer UAV deployment problems. For both primary and secondary networks, our real-time optimisation algorithms impose low computational complexity, hence, have a low execution time in solving throughput optimisation problems, which demonstrates the benefit of our approached proposed for spectrum-sharing UAV-assisted mission-critical services.
•A rumen membrane bioreactor (MBR) was developed to derive VFA from crop residues.•VFA yield of 438 mg/g substrate per day was achieved in continuous operation.•Acetic and propionic acids accounted ...for >80% of the total VFA produced.•The produced VFA was continuously transferred to a clean matrix by UF membrane.•Changes in the microbial community did not impact the rumen-MBR’s performance.
This study evaluates the feasibility of a novel rumen membrane bioreactor (rumen MBR) to produce volatile fatty acids (VFA) from crop residues (i.e. lignocellulosic biomass). Rumen MBR can provide a sustainable route for VFA production by mimicking the digestive system of ruminant animals. Rumen fluid was inoculated in a reactor coupled with ultrafiltration (UF) membrane and fed with maize silage and concentrate feed at 60:40% (w/w). Continuous VFA production was achieved at an average daily yield of 438 mg VFA/g substrate. The most abundant VFA were acetic (40–80%) and propionic (10–40%) acids. The majority (73 ± 15%) of produced VFA was transferred through the UF membrane. Shifts in dominant rumen microbes were observed upon the transition from in vivo to in vitro environment and during reactor operation, however, stable VFA yield was maintained for 35 days, providing the first proof-of-concept of a viable rumen MBR.
Display omitted
•Micropollutant degradation by oxidoreductase enzymes is reviewed.•Free and immobilized enzymes show a great potential for real wastewater treatment.•Wastewater matrix constituents ...affect enzymatic biodegradation efficiency.•Enzymatic membrane reactors facilitate operationability and reusability of enzymes.•Further research is necessary to transfer enzymatic treatment into larger scale.
Enzymatic conversion of micropollutants into less-toxic derivatives is an important bioremediation strategy. This paper aims to critically review the progress in water and wastewater treatment by both free and immobilized enzymes presenting this approach as highly efficient and performed under environmentally benign and friendly conditions. The review also summarises the effects of inorganic and organic wastewater matrix constituents on enzymatic activity and degradation efficiency of micropollutants. Finally, application of enzymatic reactors facilitate continuous treatment of wastewater and obtaining of pure final effluents. Of a particular note, enzymatic treatment of micropollutants from wastewater has been mostly reported by laboratory scale studies. Thus, this review also highlights key research gaps of the existing techniques and provides future perspectives to facilitate the transfer of the lab-scale solutions to a larger scale and to improve operationability of biodegradation processes.
Resource allocation is one important mission in wireless communication systems. In 5G wireless networks, it is essential that the new systems be more dynamic and wiser to simultaneously satisfy ...various network demands, by using new wireless technologies and approaches. To this end, resource allocation is faced with many significant challenges such as interference alignment, security attacks, or green communication. On the other hand, as one serious problem in 5G networks, the issue of energy is a?ected directly by the allocated resources in the system, i.e., bandwidth allocation, power control, association allocation, and deployment strategies. Consequently, together with the enhancement of spectral eÿciency performance, an emerging trend of 5G wireless networks is to approach green communication via energy eÿciency (EE) (bits/Hz/Joule), whose most significant challenge is due to its belonging to the fractional programming in the optimization field, i.e., nonconvex programming. This leaves many diÿcult tasks for improving network EE performance. In this paper, we will tackle the critical EE in 5G wireless networks.
Deep convolutional neural networks (CNNs) have become one of the state-of-the-art methods for image classification in various domains. For biomedical image classification where the number of training ...images is generally limited, transfer learning using CNNs is often applied. Such technique extracts generic image features from nature image datasets and these features can be directly adopted for feature extraction in smaller datasets. In this paper, we propose a novel deep neural network architecture based on transfer learning for microscopic image classification. In our proposed network, we concatenate the features extracted from three pretrained deep CNNs. The concatenated features are then used to train two fully-connected layers to perform classification. In the experiments on both the 2D-Hela and the PAP-smear datasets, our proposed network architecture produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods.
In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's reflection for ...enhancing the network's performance. To aim at maximising the energy efficiency (EE) of the considered networks, we jointly optimise the power allocation of the UAVs and the phase-shift matrix of the RIS. A deep reinforcement learning (DRL) approach is proposed for solving the continuous optimisation problem with time-varying channels in a centralised fashion. Moreover, parallel learning approach is also proposed for reducing the latency of information transmission requirement of the centralised approach. Numerical results show a significant improvement of our proposed schemes compared with the conventional approaches in terms of EE, flexibility, and processing time. Our proposed DRL methods for RIS-assisted UAV networks can be used for real-time applications due to their capability of instant decision-making and handling the time-varying channel with the dynamic environmental setting.
PDF
