Avian influenza and co-infections : investigation of the interactions in the poultry models

The purpose of this study was to assess the burden of co-infections in the field and to better understand the possible synergism between pathogens in a laboratory setting. We focussed on E. coli (O78) and low pathogenic avian influenza virus (LPAIV, H6N1) in turkey model and infected the birds via the aerosol route to reproduce respiratory disease. Viral shedding and lesions were more severe and persisted longer during coinfection indicating possible enhancement of pathogenesis for LPAIV by E. coli and vice versa. These findings all endorse our conclusions that E. coli and LPAIV exercise an additive pathogenic effect in the reproduction of respiratory disease if given simultaneously or spaced by three days between the viral and the bacterial challenges to susceptible turkeys. In parallel, we studied avian respiratory agents circulating in the field in Pakistani farms. There, we focussed on co-infections as well, targeting viruses only as a first study. We observed frequent LPAIV H9 (G1 lineage) and Newcastle disease virus (genotype VII) coinfections in the field

User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes

Impact of Capital Structure on Firms’ Financial Performance: Evidence from Pakistan

This research examines the impact of capital structure on firms’ financial performance in Pakistan of top 100 consecutive companies in Karachi Stock Exchange for a period of four years from 2006 to 2009. Exponential generalized least square regression is used to test the relationship between capital structure and firms’ financial performance. The results show that all the three variables of capital structure, Current Liabilities to Total Asset, Long Term Liabilities to Total Asset, Total Liabilities to Total Assets, negatively impacts the Earning before Interest and Taxes, Return on Assets, Earning per Share and Net Profit Margin whereas Price Earning ratio shows negative relationship with Current Liabilities to Total Asset and positive relationship is found with Long Term Liabilities to Total Asset where the relationship is insignificant with , Total Liabilities to Total Assets. The results also indicate that Return on Equity has an insignificant impact on Current Liabilities to Total Asset and Total Liabilities to Total Assets but a positive relationship exists with Long Term Liabilities to Total Asset. These results, in general, lead to the conclusion that capital structure choice is an important determinant of financial performance of firms. This is the first study in Pakistan examining the relationship between firms’ performance and capital structure of top 100 consecutive companies in Karachi Stock Exchange for a period of four years. Keywords: Capital Structure, Firms’ Performance, Performance Measures, Profit, Pakistan

An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification

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A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

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Internet of Things-Based Fire Alarm Navigation System: A Fire-Rescue Department Perspective

In the past few years, fire alarm systems have become increasingly sophisticated and more capable and reliable. The two main objectives are the protection of life and property. As a result of state and local codes, fire protection has become more concerned with life safety over the past two decades. Several safety measures have been implemented to address the problems caused by the fires and reduce the number of fatalities and property damage. Our project is to develop and review a fire alarm navigation system and application that uses the internet of things. Fire alarm systems are designed to warn people about fires in advance so that they can evacuate the fire-affected area and take immediate action to control the fire. There will be a GPS module, a flame sensor, a smoke sensor, buzzers, LEDs, and a GSM module to ensure early notification to authorities and fire stations. The aim is to reduce the loss of lives and property. A questionnaire was designed to conduct a brief survey in a multinational sports production company in Sialkot, Pakistan, regarding the IoT fire alarm navigation system. Besides installing the system in the factory, we compare the results with fire incident response time with and without this system at rescue 1122 fire head station.publishedVersio

A New Correlation Coefficient for T-Spherical Fuzzy Sets and Its Application in Multicriteria Decision-Making and Pattern Recognition

The goal of this paper is to design a new correlation coefficient for -spherical fuzzy sets (TSFSs), which can accurately measure the nature of correlation (i.e., positive and negative) as well as the degree of relationship between TSFS. In order to formulate our proposed idea, we had taken inspiration from the statistical concept of the correlation coefficient. While doing so, we firstly introduce the variance and covariance of two TSFS and then constructed our scheme using these two newly defined notions. The numerical value of our proposed correlation coefficient lies within the interval , as it should be from a statistical point of view, whereas the existing methods cannot measure the negative correlation between TSFS, as their numerical value falls within the interval , which is not reasonable both statistically and intuitively. This aspect has also been thoroughly demonstrated using some numerical examples. The comparison results witnessed the dominance and upper hand of our proposed method over the existing definitions, with reliable and better results. In order to demonstrate the feasibility, usefulness, and practical application, we applied our proposed scheme to solve technical and scientific problems of multicriteria decision-making and pattern recognition. The numerical results show that our proposed scheme is practically suitable, technically applicable, and intuitively reasonable.publishedVersio

In the Direction of Service Guarantees for Virtualized Network Functions

The trend of consolidating network functions from specialized hardware to software running on virtualization servers brings significant advantages for reducing costs and simplifying service deployment. However, virtualization techniques have significant limitations when it comes to networking as there is no support for guaranteeing that network functions meet their service requirements. In this paper, we present a design for providing service guarantees to virtualized network functions based on rate control. The design is a combination of rate regulation through token bucket filters and the regular scheduling mechanisms in operating systems. It has the attractive property that traffic profiles are maintained throughout a series of network functions, which makes it well suited for service function chaining. We discuss implementation alternatives for the design and demonstrate how it can be implemented on two virtualization platforms: LXC containers and the KVM hypervisor. To evaluate the design, we conduct experiments where we measure throughput and latency using IP forwarders (routers) as examples of virtual network functions. Two significant factors for performance are investigated: the design of token buckets and the packet clustering effect that comes from scheduling. Finally, we demonstrate how performance guarantees are achieved for rate-controlled virtual routers under different scenarios.publishedVersio

Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.publishedVersio