Investigation on sulphonated zinc oxide nanorod incorporated sulphonated poly (1,4-phenylene ether ether sulfone) nanocomposite membranes for improved performance of microbial fuel cell

Hydrothermally prepared zinc oxide nanorods are sulphonated (S–ZnO NR) and incorporated into 15% Sulphonated Poly (1,4-Phenylene Ether Ether Sulfone) (SPEES) to improve the hydrophilicity, water uptake and ion transfer capacity. Water uptake and ion transfer capacity increased to 34.6 ± 0.6% and 2.0 ± 0.05 meq g^?1 from 29.8 ± 0.3% and 1.4 ± 0.04 meq g^?1 by adding 7.5 wt% S–ZnO NR to SPEES. Morphological studies show the prepared S–ZnO NR is well dispersed in the polymer matrix. SPEES +7.5 wt% S–ZnO NR membrane exhibits optimum performance after three-weeks of continual operation in a fabricated microbial fuel cell (MFC) to produce a maximum power density of 142 ± 1.2 mW m^?2 with a reduced biofilm compared to plain SPEES (59 ± 0.8 mW m^?2), unsulphonated filler incorporated SPEES (SPEES + 7.5 wt% ZnO, 68 ± 1.1 mW m^?2) and Nafion (130 ± 1.5 mW m^?2) thereby suggesting its suitability as a sustainable and improved cation exchange membrane (CEM) for MFCs.     

Bioconvective Casson nanofluid flow toward stagnation point in non-Darcy porous medium with buoyancy effects,chemical reaction,and thermal radiation

The emphasis on non-Newtonian fluid encountered in biomedical, pharmaceuticals, mining, food, chemical, and plastics industries and in noticeably enormous diverse industrial applications influenced this article. This study is accomplished in a non-Darcy porous stretching surface to investigate the stagnation point of bioconvective Casson nanofluid. Chemical reaction, applied consistent magnetic field, radiative heat transfer, and buoyancy force consequences are studied for numerical examination. Composed of nonlinear partial differential equations for the above presumptions and reforming them into ordinary differential equations by means of compatible transformations are enforced. Adopting the fifth order Runge–Kutta Felhberg method with the shooting technique obtained a numerical solution. Obtained solutions are authenticated by comparing previous solutions. The major finding includes the reduction of the Casson parameter on the skin friction coefficient.     

Escherichia coli, enterococci, and Bacteroides thetaiotaomicron qPCR signals through wastewater and septage treatment

Fecal indicators such as Escherichia coli and enterococci are used as regulatory tools to monitor water with 24 h cultivation techniques for possible input of sewage or feces and presence of potential enteric pathogens yet their source (human or animal) cannot be determined with routine methods. This critical uncertainty has furthered water pollution science toward new molecular approaches. Members of Bacteroides genus, such as Bacteroides thetaiotaomicron are found to have features that allow their use as alternative fecal indicators and for Microbial Source Tracking (MST). The overall aim of this study was to evaluate the concentration and fate of B. thetaiotaomicron, throughout a wastewater treatment facility and septage treatment facility. A large number of samples were collected and tested for E. coli and enterococci by both cultivation and qPCR assays. B. thetaiotaomicron qPCR equivalent cells (mean: 1.8 × 10⁷/100 mL) were present in significantly higher concentrations than E. coli or enterococci in raw sewage and at the same levels in raw septage. The removal of B. thetaiotaomicron target qPCR signals was similar to E. coli and enterococci DNA during the treatment of these wastes and ranged from 3 to 5 log₁₀ for wastewater and was 7 log₁₀ for the septage. A significant correlation was found between B. thetaiotaomicron marker and each of the conventional indicators throughout the waste treatment process for both raw sewage and septage. A greater variability was found with enterococci when compared to E. coli, and CFU and equivalent cells could be contrasted by various treatment processes to examine removal and inactivation via septage and wastewater treatment. These results are compared and contrasted with other qPCR studies and other targets in wastewater samples providing a view of DNA targets in such environments.     

Performance Analysis of Downlink W-CDMA Systems in Weibull and Lognormal Fading Channels Using Chaotic Codes

In this paper, a subspace based blind channel estimation scheme for downlink W-CDMA systems using chaotic codes under Weibull and Lognormal fading channel conditions is proposed and compared with W-CDMA system using PN codes. The algorithm provides estimates of multiuser channels by exploiting the structural information of the data output. The subspace of the (data + noise) matrix contains sufficient information for unique determination of channels and, hence, the signature waveforms and signal constellations. The proposed channel estimation algorithm is also implemented for multiuser—orthogonal frequency division multiplexing (OFDM) system. Performance measures like bit error rate (BER) and root mean square error (RMSE) are plotted for Weibull and Lognormal fading channels. Signal constellations under Weibull and Lognormal channels are also plotted. Analytical and Simulation results for BER and RMSE are compared for W-CDMA system using PN codes and chaotic codes. Simulation results show that, chaos-based W-CDMA outperforms the PN-based W-CDMA in terms BER and RMSE. Simulation results of multiuser-OFDM system shows that performance is further improved when compared to the W-CDMA system.     

Close-range blast loading on empty recyclable metal beverage cans for use in sacrificial cladding structure

This article deals with the experimental and numerical blast study on a single empty recyclable metal beverage can. The idea is to make a macro-foam (sacrificial cladding structure) out of these cans to protect the main load bearing members of civil engineering structures from the air blast load. Close-range free air blast tests have been conducted to understand the crushing behaviour and the energy absorption of a single empty beverage can in detail. To conduct such an air blast test a special small-scale test set-up was designed and manufactured. The effect of skin plate surface area and its nature on the blast parameters is studied. Furthermore, the effect of inertia of the skin plate on the crushing performance of the beverage can is evaluated. Tests have been conducted with different plates (made of aluminium and sandwich composite materials) with different masses which represent the skin plate of the proposed sacrificial structure. The measured blast parameters from the experimental tests were compared with ConWep predicted data. Furthermore, the influence of the finite surface area of the skin plate on the clearing of the reflected pressure waves was also studied. The deformation behaviour and the corresponding energy absorption of empty beverage cans were captured. During the experimental blast tests it was observed that a part of the total reflected impulse (∼30%) was lost before it transfers to the non-sacrificial structure. Hence, in order to investigate this phenomenon Eulerian-Lagrangian coupled analyses were conducted using Hydrocodes. The results from these analyses showed that the diffraction and ground reflected pressure wave caused the loss in impulse. The results from the numerical studies have been compared and validated with the experimental results.     

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

The non-orthogonal multiple access (NOMA) wireless networks are a robust multi-access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth-generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA-UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA-UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic-based crossover-based coati optimization algorithm for maximizing the energy efficiency of NOMA-UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision-making strategy optimizes the energy efficiency (EE) of NOMA-UAV by selecting appropriate power and time resources. Additionally, the crossover-based coati optimization algorithm transforms the subchannel allocation problem into a two-sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA-UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.     

A strategy to reduce the control packet load of MANETs with bidirectional links using DSR

Dynamic source routing (DSR) is a robust protocol commonly applied to multi‐hop wireless mobile ad hoc networks (MANETs). In this paper, an algorithm is proposed for modifying the basic DSR protocol to enhance its performance by reducing the number of redundant route reply packets (RREPs). In the modified DSR (MDSR), for a source destination pair, the destination responds to the first received route request packet (RREQ) with an RREP, and the subsequently received RREQs, bearing the same request ID, are responded to only if the hop count is less than that of all the previously received RREQs. The performance of MDSR has been compared with that of the basic DSR for different network densities and for different mobility of nodes. Simulation results show that MDSR gives fewer control packets, less latency and a higher packet delivery ratio than DSR. Copyright © 2007 John Wiley & Sons, Ltd.     

ZnO energy transfer and enhanced photoluminescence in MEH-PPV/ZnO hybrid nanocomposite

Hybrid organic–inorganic nanocomposites are great candidates for display and illumination systems due to improved optoelectronic properties and photostability. This work endeavours towards the scientific study of the influence of defect-induced zinc oxide nanoparticles (ZnO) on the optical characteristics of poly[2-methoxy-5-(2′-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV). ZnO nanoparticles consist of many vacancies which facilitate light emission across the visible region. The green defective emission occurring due to the presence of oxygen vacancies in ZnO was used to re-excite MEH-PPV and hence, improve the luminescence quantum efficiency. The photostability of the nanocomposite was enhanced through charge transfer (prevents the formation of superoxides) and energy transfer (reduces the non-radiative decay) mechanisms.

Graphical abstract

     

An innovative hybrid approach for extracting named entities from unstructured text data

Named entity recognition (NER) is the core part of information extraction that facilitates the automatic detection and classification of entities in natural language text into predefined categories, such as the names of persons, organizations, locations, and so on. The output of the NER task is crucial for many applications, including relation extraction, textual entailment, machine translation, information retrieval, etc. Literature shows that machine learning and deep learning approaches are the most widely used techniques for NER. However, for entity extraction, the abovementioned approaches demand the availability of a domain‐specific annotated data set. Our goal is to develop a hybrid NER system composed of rule‐based deep learning as well as clustering‐based approaches, which facilitates the extraction of generic entities (such as person, location, and organization) out of natural language texts of domains that lack generic named entities labeled domain data sets. The proposed approach takes the advantages of both deep learning and clustering approaches but separately, in combination with a knowledge‐based approach by using a postprocessing module. We evaluated the proposed methodology on court cases (judgments) as a use case since it contains generic named entities of different forms that are poorly or not present in open‐source NER data sets. We also evaluated our hybrid models on two benchmark data sets, namely, Computational Natural Language Learning (CoNLL) 2003 and Open Knowledge Extraction (OKE) 2016. The experimental results obtained from benchmark data sets show that our hybrid models achieved substantially better performance in terms of the F‐score in comparison to other competitive systems.