A novel dynamic software-defined networking approach to neutralize traffic burst

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. © 2023 by the authors

Seroprevalence of antistreptolysin O antibodies in a tertiary health care centre in Haryana, India: a three year retrospective study

Background: Presence of antistreptolysin O antibodies in a patient’s sera may be an isolated evidence of recent infection by group A or less commonly, group C or G Streptococcus, especially in patients suspected of having a non suppurative sequel to this infection.Methods: A retrospective study was done on the sera samples received in the Department of Microbiology, PGIMS Rohtak, India for the detection of ASO, over a period of three years. The test was carried out by latex agglutination rapid test kit by Aspen.Results: A total of 4632 samples were received in the laboratory during the study period. Of these, 1058 (22.8%) were found to be positive for the presence of ASO having titre of >200 IU/mL.Conclusions: The prevalence of ASO was found to be highest in the age group 0-20. The presence of elevated streptococcal antibody titres in such a population reflects a high background prevalence of streptococcal infections. Thus, determination of ASO antibodies should be taken into consideration when evaluating the role of group A streptococcus in non-purulent complications of infections

Study on pattern of consumption of fruits and vegetables and associated factors among medical students of Delhi

Background: Fruits and vegetables are a rich source of essential micronutrients i.e. vitamins/minerals and dietary fibers required for the normal daily functionality of the body. Young adults such as medical students are a particularly vulnerable population in terms of health issues and adequate diet. Objective of the study was to find the pattern of fruits and vegetables consumption in undergraduate medical students of Delhi.Methods: A cross-sectional study was planned among 300 undergraduate students from medical college in New Delhi. The questionnaire consisted of questions about identification data, pattern of fruit and vegetable consumption. Data was analyzed by SPSS software version 21.0 and for qualitative data analysis chi-square test was used.Results: Mean age of study subjects was 20.82±2.1 years and females (52.7%) were more as compared to (47.3%) males. Out of 300 participants, only one third (33.3%) of study participants consumed more than five servings of fruits and vegetables. More than half of study participants felt that unsafe use of pesticides, difficult to eat five servings in a day, poor handling and poor quality of fruits and vegetables were the most common barriers in consumption of FVs. Age and semester of study participants and education status of mothers were found significant predictors of consumption of recommended number of serving of FVs in day.  Conclusions: This study concludes that only one third of study participants consumed more than five servings of fruits and vegetables which is recommended number of serving in a day. So, there is a need to increase awareness about importance of fruits and vegetables consumption among study population

Multi-objective optimization of TW-ECSM process parameters for machining of advanced non-conducting material

52-61Travelling wire electrochemical spark machining (TW-ECSM) is newly evolved and developed hybrid machining process for the machining of advanced non-conducting materials which possess significant values of the properties like high strength, high wear and fatigue resistance, high refractoriness and high strength to weight ratio, etc. The control parameters like voltage, wire feed rate, electrolyte concentration and inter-electrode gap were selected as Input Parameters and Material Removal Rate (MRR), and Surface Roughness (SR) were the corresponding output responses. In present work, for multiobjective optimization and purpose of better control of machining parameters, three approaches, grey-relational analysisprincipal component analysis (GRA-PCA), fuzzy logic and desirability function approach are used to determine the optimal combination of TW-ECSM process variables. Results of fuzzy logic and GRA-PCA approach are found comparable while desirability function approach is found to be capable of predicting the optimal responses at such levels of process variables also at which experiments are not performed. Consequences of the applied approach in the present work are also validated by conducting the confirmatory experiments and results are found in well agreement with the predicted results

Molecular surveillance of insecticide resistance in Phlebotomus argentipes targeted by indoor residual spraying for visceral leishmaniasis elimination in India

Molecular surveillance of resistance is an increasingly important part of vector borne disease control programmes that utilise insecticides. The visceral leishmaniasis (VL) elimination programme in India uses indoor residual spraying (IRS) with the pyrethroid, alpha-cypermethrin to control Phlebotomus argentipes the vector of Leishmania donovani, the causative agent of VL. Prior long-term use of DDT may have selected for knockdown resistance (kdr) mutants (1014F and S) at the shared DDT and pyrethroid target site, which are common in India and can also cause pyrethroid cross-resistance. We monitored the frequency of these marker mutations over five years from 2017–2021 in sentinel sites in eight districts of north-eastern India covered by IRS. Frequencies varied markedly among the districts, though finer scale variation, among villages within districts, was limited. A pronounced and highly significant increase in resistance-associated genotypes occurred between 2017 and 2018, but with relative stability thereafter, and some reversion toward more susceptible genotypes in 2021. Analyses linked IRS with mutant frequencies suggesting an advantage to more resistant genotypes, especially when pyrethroid was under-sprayed in IRS. However, this advantage did not translate into sustained allele frequency changes over the study period, potentially because of a relatively greater net advantage under field conditions for a wild-type/mutant genotype than projected from laboratory studies and/or high costs of the most resistant genotype. Further work is required to improve calibration of each 1014 genotype with resistance, preferably using operationally relevant measures. The lack of change in resistance mechanism over the span of the study period, coupled with available bioassay data suggesting susceptibility, suggests that resistance has yet to emerge despite intensive IRS. Nevertheless, the advantage of resistance-associated genotypes with IRS and under spraying, suggest that measures to continue monitoring and improvement of spray quality are vital, and consideration of future alternatives to pyrethroids for IRS would be advisable

Nationally Appropriate Mitigation Actions (NAMAs) – The Future Course

34-36The Conference of Parties (COP21) at Paris in December is crucial as it is expected that countries will reach an agreement on the new international climate architecture where Nationally Appropriate Mitigation Actions (NAMAs) will have a significant role to play

Climate Refugees: A Disremembered Concern!

14-19With changing climate forcing people to displace and migrate to other countries, India should seriously consider the upcoming threat of climate refugees

Machine learning-based optimal load balancing in software-defined networks

The global advancement of the Internet of Things (IoT) has poised the existing network traffic for explosive growth. The prediction in the literature shows that in the future, trillions of smart devices will connect to transfer useful information. Accommodating such proliferation of devices in the existing network infrastructure, referred to as the traditional network, is a significant challenge due to the absence of centralized control, making it tedious to implement the device management and network protocol updates. In addition, due to their inherently distributed features, applying machine learning mechanisms in traditional networks is demanding. Consequently, it leads to an imbalanced load in the network that affects the overall network Quality of Service (QoS). Expanding the existing infrastructure and manual traffic control methods are inadequate to cope with the exponential growth of IoT devices. Therefore, an intelligent system is necessary for future networks that can efficiently organize, manage, maintain, and optimize the growing networks. Software-defined network (SDN) has a holistic view of the network and is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers that faces severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (SDN) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, the research was enhanced with a priority scheduling and congestion control algorithm in the standard SDN, named extended SDN (eSDN), which minimized the network congestion and performed better than the existing SDN. However, enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic controller mapping in the network. Often, the same controller gets overloaded, leading to a single point of failure. Our exhaustive literature review shows that the majority of proposed solutions are based on static controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among controllers in real-time, eventually increasing the network latency. Often, the switch experiences a traffic burst, and consequently, the corresponding controller might overload. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static controller to neutralize the on-the-fly traffic burst. Addressing the above-mentioned issues demands research critical to improving the QoS in load balancing, latency minimisation, and network reliability for next- generation networks. Our novel dynamic controller mapping algorithm with multiple- controller placement in the SDN is critical in solving the identified issues. In the dynamic controller approach (dSDN), the controllers are mapped dynamically as the load fluctuates. If any controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. In addition, our novel approach adds more intelligence to the network with a Temporal Deep Q Learning (tDQN) approach for dynamic controller mapping when the flow fluctuates. In this technique, a multi-objective optimization problem for flow fluctuation is formulated to dynamically divert the traffic to the best-suited controller. The formulated technique is placed as an agent in the network controller to take care of all the routing decisions, which can solve the dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. Extensive simulation results show that the novel approach proposed in this thesis solves dynamic flow mapping by maintaining a balanced load among controllers and outperforms the existing traditional networks and SDN with priority scheduling and congestion control. Compared to traditional networks, tDQN provides a 47.48% increase in throughput, a 99.10% reduction in delay and a 97.98% reduction in jitter for heavy network traffic. The thesis also presents a few future research directions as possible extensions of the current work for further enhancement.Doctor of Philosoph

Physics based modeling of LiFePO4 cathodes: effects of electrode parameters on cell performance during fast charging

Lithium-iron phosphate (LFP) has emerged as a potential cathode material due to its lower cost and higher stabilities. This work investigates LFP cell behavior at higher C-rates via a detailed simulation study. To facilitate this investigation, a physics-based electrochemical model is calibrated and validated with in-house experimental data. The validated model is used to study the effect of particle size, lithium diffusivity, and electrode thickness on the charge-discharge capacity of Li-LFP cells for a range of C-rates up to 5 C. A detailed discussion is carried out to explain the results of parametric studies, in terms of transport limitations, irreversible losses (overpotentials) and their dependence on different electrode parameters. The model helps us to depict the effect of these parameters on internal profiles of SOC and overpotentials, allowing for a deeper understanding of the cell behavior. Overall, the simulations show that the LFP cell is able to exhibit good capacity at higher C-rates by tuning the particle size and lithium diffusivity. An optimal combination of material and physical parameters is identified to maximize the possible capacity of LFP electrodes