Synthesis,characterization and use of iron oxide nano particles for antibacterial activity

In this decade, the use of nano particles (NPs) against bacterial growth is increasing day by day due to remarkable alternative properties compared to molecular antibiotics. Thus, the use of iron oxide nanoparticles (IONPs) has proven one of the most important transition metals oxide‐based remedy in nanotechnological advances and biological applications due to enriched biocompatibility of iron. In this study synthesis of IONPs was carried out via co‐precipitation method. The crystallographic morphology of the synthesized particles was studied via X‐ray diffraction which revealed cubic structure of the particles, whereas, the spinal shaped morphology of the prepared NPs was confirmed from scanning electron microscopy. Likewise, the presence of the major elements in the sample was determined through energy dispersive X‐ray analysis characterization. Bactericidal effect of the NPs was assessed at pre‐defined concentrations (50 and 100 μg/ml) against Gram +ve bacteria Staphylococcus aureus, Gram ?ve bacteria Shigella dysentry and Escherichia coli . Bacterial strains, which demonstrate the potential of NPs. The purpose of this study was assessing the structure of the synthesized NPs for protective effect against harmful bacterial activity.     

Numerical study of mixed convection nanofluid in an annulus enclosure between outer rotating cylinder and inner corrugation cylinder

Numerical investigations are presented for mixed convection problems in a concentric inner sinusoidal cylinder and an outer rotating circular cylinder, which were kept at constant hot and cold temperatures, respectively. The free space between the cylinders and the enclosure walls was filled with a water‐Cu nanofluid. The governing equations are formulated for velocity, pressure, and temperature formulation and are modeled in COMSOL5.2a, a partial differential equation solver based on the Galerkin finite element method. The governing parameters considered are the solid volume fraction, [0, 0.02, 0.04, and 0.06], Re (1, 25, 100, 200, and 300), and Ra (less than 10⁴), and the inner cylinder corrugation frequencies varied from (N = 3, 6, and 9). According to the calculations, the Reynolds number, the Rayleigh number, the nanoparticle volume fraction, and the number of corrugations play an important role of forming the stream and isothermal lines, the local and the average Nusselt number inside the annulus enclosure. The average Nusselt number decreases with increasing Reynolds number and the number of corrugations, while it increases as the Rayleigh number and the volume fraction increase.     

An Innovative Approach to the Synthesis of PMMA/PEG/Nanobifiller Filled Nanocomposites with Enhanced Mechanical and Thermal Properties

A facile route was adopted to blend the matrix. The PMMA/PEG blend was reinforced with three types of nanofillers, i.e., pristine MWCNT (P-CNT), amine functionalized MWCNT (PDA-EA-CNT) and nanobifiller i.e. nanodiamond functional MWCNT (PDA-EA-CNT-ND) to yield three different types of nanocomposites i.e. PMMA/PEG/P-CNT, PMMA/PEG/PDA-EA-CNT and PMMA/PEG/PDA-EA-CNT-ND. These nanocomposites were reinforced with nanofiller loading (1 wt. %, 3 wt. %, 5 wt. %, 10 wt. %, 30 wt. % and 50 wt. %) by solution casting method. Structure of composite and nanofillers was confirmed by FTIR. FESEM imaging revealed that nanocomposites have micro porous morphology. At high magnification, distribution of functionalized CNT/ND appears to be protruding out of the polymeric matrix. The TGA result suggests that the thermal stability of the nanocomposites was enhanced in comparison to PMMA due to grafting of filler molecules with PMMA/PEG macromolecules. The DTG results showed that the bifiller nanocomposites (PMMA/PEG/PDA-EA-CNT-ND) exhibited improved thermal stability with T_max (431^°C) as compared to P-CNT and amine functionalized CNT (PMMA/PEG/PDA-EA-CNT) with T_max of 395^°C and 418^°C respectively. XRD results showed fine interaction between filler and the polymeric matrix. As the filler loading was increased the composites showed pronounced XRD peak at 25.9^°, corresponding to (002) reflection of nanotubes. Significant improvement in the mechanical properties of composites was recorded with the reinforcement of fillers as compared to the neat matrix. The most significant improvement in tensile strength and elastic modulus was observed for the bifiller nanocomposites with 5 wt. % PDA-EA-CNT-ND. They showed a tensile strength and elastic modulus of 29.9 MPa and 1474.31 MPa respectively as compared to amine functionalized CNT with tensile strength (25.7) and elastic modulus (1466.99 MPa)and P-CNT with tensile strength(25 MPa) and elastic modulus (1155.75 MPa).     

Towards a Secure IPv6 Autoconfiguration

ABSTRACT

Stateless Address Autoconfiguration (SLAAC) is one of the novel features introduced in IPv6 Neighbor Discovery Protocol (NDP) providing for self-configuration of nodes and supplementing the reduction of operational and deployment cost of networks. Although self-configuration elevates the idea of autonomy for network, it has also introduced vulnerabilities that require substantial solutions. The SLAAC is pivoted on the presumption that network consists of authentic and entrusted nodes, however with inception of public sector wireless networks; any node can affix to the link with trivial authentication and situation changes radically. Although some security extensions like IPsec or SeND have been proposed, but these security protocols have been reported to have serious limitations like complex cryptographic algorithms which negate their adoption. This paper revisits the stateless auto configuration process and discusses its inherent vulnerabilities. The paper surveys existing research and available defense mechanisms available to protect SLAAC. The paper also suggests some guidelines from existing literature which can further promote and supplement the research to secure the auto configuration process. Finally, a novel technique is proposed for securing IPv6 link layer communication against DoS and Man-in-the-Middle attack which can be used as an alternate approach for CGA and SeND protocol.     

EBSD characterization of carbide–carbide boundaries in WC–Co composites

A sample of WC‐6wt%Co was investigated for grain boundary character distribution and occurrence of coincidence site lattice (CSL) boundaries on a statistical basis. For this purpose orientation measurements of the grains were carried out using electron back‐scattered diffraction (EBSD). The dominant misorientation relationships were determined by complementary EBSD data representation tools such as orientation maps, misorientation angle distribution histograms and the sectioned three‐dimensional misorientation space. It was found that the grain boundary character distribution of the material is nearly random and the CSL boundaries are not present in statistically significant amounts. It was also found that the amount of binder phase does not play a role in the formation of special boundaries. The paper focuses on the methodology of characterizing grain boundaries in a hexagonal material using EBSD.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.     

Optimizing Irrigation Deficit of Multipurpose Cascade Reservoirs

Reservoirs play a strategic role in the rapid monetary growth of the world by providing numerous benefits. However, the reduction in appropriate sites along with environmental and social apprehensions has resulted in curtailment of new reservoirs around the world in twenty-first century. There is a potential of benefits available from existing reservoirs which can be best capitalized through their optimized operation. Reservoirs Operation Optimization considering Sediment Evacuation (RESOOSE), recently developed model which combines multiple reservoirs operation and sediment evacuation with Genetic Algorithm based optimization module, has been used in the study. The objective of the study was to optimize the irrigation deficit through cascade reservoirs with consideration to hydropower, sediment evacuation and flood damages reduction benefits. The RESOOSE model was applied to optimize the irrigation deficits of Tarbela and Diamer Basha Reservoirs in Pakistan using developed objective function. The article computed and compared the benefits of optimized and existing rule curves. The hydropower benefits of 36.92 Billion Kw, sediment evacuation benefits of 21.534 Million m³ and flood damages of 616.19 Million US$ due to existing rule curves were considered as minimum benefits for achieving the optimized rule curves to minimize irrigation deficits. The developed optimized rule curves reduced the irrigation shortages of case study reservoirs from 6.9 to 5.8 Billion m³ (16% enhancement) annually as compared to existing rule curves. The optimized rule curves minimized the irrigation deficits by maintaining the existing benefits and without lowering the minimum operating levels of case study reservoirs. The study suggests change in existing rule curves of Tarbela and Diamer Basha Reservoirs due to less irrigation shortages. The RESOOSE model can be applied to other cascade reservoirs for optimizing the rule curves.     

Mapping past,current and future energy research trend in Pakistan: a scientometric assessment

This work describes the contribution of researchers in the field of the energy from Pakistan in the period 1990–2016. A scientometric approach was applied to analyze the scientific publications in the field using the Scopus Elsevier database. Different aspects of the publications were analyzed, such as publication type, major research areas, journals, citations, authorship pattern, affiliations as well as the keyword occurrence frequency. The present research trends are analyzed and future research directions are outlined. The impact factor, h-index and number of citations were used to investigate the strength of active institutes, authors, and journals in the field of the energy in Pakistan. From 1990 to 2016, 991 articles have been published by 2139 authors from 213 research institutes. The total number of citations and impact factor are 10,287 and 2301 respectively, corresponding to 10 citations per paper and an impact factor of 2.32 per publication. The research articles originate primarily from COMSATS, NUST, PIEAS, and PINSTECH. Pakistan has published 60% of publication with the collaboration of the foreign institutes, mainly from the United States, the United Kingdom, China and Malaysia. The core research activities in the field are mainly focused on resource assessment, energy policy, energy efficiency, feasibility study, energy economics, and performance assessment. The most productive journal, author, institution, are renewable & sustainable energy review, Shahbaz M., and COMSATS, respectively.