Resonant oscillations of a plate in an electrically conducting rotating Johnson-Segalman fluid

AbstractAn analysis of hydromagnetic flow is examined in a semi-infinite expanse of electrically conducting rotating Johnson-Segalman fluid bounded by nonconducting plate in the presence of a transverse magnetic field and the governing equations are modeled first time. The structure of the velocity distribution and the associated hydromagnetic boundary layers are investigated including the case of resonant oscillations. It is shown that unlike the hydrodynamic situation for the case of resonance, the hydromagnetic steady solution satisfies the boundary condition at infinity. The inherent difficulty involved in the hydrodynamic resonance case has been resolved in the presence analysis

Task bundling in worker‐centric mobile crowdsensing

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks

Scrutinizing human MHC polymorphism:supertype analysis using Poisson-Boltzmann electrostatics and clustering

Peptide-binding MHC proteins are thought the most variable proteins across the human population; the extreme MHC polymorphism observed is functionally important and results from constrained divergent evolution. MHCs have vital functions in immunology and homeostasis: cell surface MHC class I molecules report cell status to CD8+ T cells, NKT cells and NK cells, thus playing key roles in pathogen defence, as well as mediating smell recognition, mate choice, Adverse Drug Reactions, and transplantation rejection. MHC peptide specificity falls into several supertypes exhibiting commonality of binding. It seems likely that other supertypes exist relevant to other functions. Since comprehensive experimental characterization is intractable, structure-based bioinformatics is the only viable solution. We modelled functional MHC proteins by homology and used calculated Poisson-Boltzmann electrostatics projected from the top surface of the MHC as multi-dimensional descriptors, analysing them using state-of-the-art dimensionality reduction techniques and clustering algorithms. We were able to recover the 3 MHC loci as separate clusters and identify clear sub-groups within them, vindicating unequivocally our choice of both data representation and clustering strategy. We expect this approach to make a profound contribution to the study of MHC polymorphism and its functional consequences, and, by extension, other burgeoning structural systems, such as GPCRs

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets

Blockchain-enabled Reliable Osmotic Computing for Cloud of Things: Applications and Challenges

Cloud of Things (CoT) refers to an IoT solution consuming the cloud services of a single cloud vendor. In this paper, we have introduced the concept of a MultiCoT1 solution which refers to the collaborative execution of an IoT solution by multiple cloud vendors. Cloudlets and ad-hoc clouds are the extensions of centralized cloud services, closer to the user, in the form of fog and edge computing layers respectively and the Osmotic Computing (OC) serves as a glue by accomplishing the seamless compute sharing across these layers. The OC can also be integrated within a MultiCoT solution for extending it across three computational layers of cloud, fog and edge. However, this can only be achieved after establishing enough trust among all the vendors that are working in collaboration to simultaneously serve a particular MultiCoT solution. Blockchain has been already proven for establishing trust and supporting reliable interactions among independently operating entities. Hence, it can be used for establishing trust among the multiple cloud vendors serving a single MultiCoT solution. In this paper, we have presented the importance of using the proactive Blockchain-enabled Osmotic Manager (B-OM) for improving the reliability of OC. We have also highlighted the blockchain features that can improve the reliability of OC by establishing trust among the independently operating vendors of a MultiCoT solution, followed by the challenges associated with the integration of blockchain and OC along with the future research directions for achieving the proposed integration. © 2020 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

SDN-Sim: Integrating System Level Simulator with Software Defined Network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. With the introduction of diverse technology paradigms in next-generation cellular and vehicular networks, design and structural complexity are skyrocketing. The beyond- 5G use cases such as mobile broadband, 5G-V2X and UAV communications require support for ultra-low latency and high throughput and reliability with limited operational complexity and cost. These use cases are being explored in 3GPP Release 16 and 17. To facilitate end-to-end performance evaluation for these applications, we propose SDN-Sim - an integration of a System Level Simulator (SLS) with a Software Defined Network (SDN) infrastructure. While the SLS models the communication channel and evaluates system performance on the physical and data link layers, the SDN performs network and application tasks such as routing, load balancing, etc. The proposed architecture replicates the SLS-defined topology into an SDN emulator for offloading control operations. It uses link and node information calculated by the SLS to compute routes in SDN and feeds the results back to the SLS. Along with the architecture, data modeling and processing, replication and route calculation frameworks are proposed

Pilot-scale recovery of low molecular weight organic acids from anaerobically treated palm oil mill effluent (POME) with energy integrated system

Low molecular weight organic acids such as acetic acid, propionic acid and butyric acids generated from partial anaerobic treatment of palm oil mill effluent (POME) were recovered using pilot scale filtration and evaporation system. Mechanical filter press (14 L) was used for removing solid fractions and fraction distillation unit (40 L) for evaporation and clarification of concentrated acid from POME. Clarification using rotary evaporator was found to be more suitable than distillation column. Due to the presence of more than 90% of water in POME, the final clarified product comprises only 7% of the total volume. The material balance for the overall process was estimated and integrated system for the bioconversion of organic acids into polyhydroxyalkanoates (PHA) was proposed. The recovery of organic acids has a significant and economical impact, since around 50% cost of PHA production is believed to be associated with the substrate itself

Maternal deaths in Pakistan : intersection of gender, class and social exclusion.

Background: A key aim of countries with high maternal mortality rates is to increase availability of competent maternal health care during pregnancy and childbirth. Yet, despite significant investment, countries with the highest burdens have not reduced their rates to the expected levels. We argue, taking Pakistan as a case study, that improving physical availability of services is necessary but not sufficient for reducing maternal mortality because gender inequities interact with caste and poverty to socially exclude certain groups of women from health services that are otherwise physically available. Methods: Using a critical ethnographic approach, two case studies of women who died during childbirth were pieced together from information gathered during the first six months of fieldwork in a village in Northern Punjab, Pakistan. Findings: Shida did not receive the necessary medical care because her heavily indebted family could not afford it. Zainab, a victim of domestic violence, did not receive any medical care because her martial family could not afford it, nor did they think she deserved it. Both women belonged to lower caste households, which are materially poor households and socially constructed as inferior. Conclusions: The stories of Shida and Zainab illustrate how a rigidly structured caste hierarchy, the gendered devaluing of females, and the reinforced lack of control that many impoverished women experience conspire to keep women from lifesaving health services that are physically available and should be at their disposal