41 research outputs found
Highly sensitive biosensor based on a microstructured photonic crystal fibre for alcohol sensing
This research article was published by Elsevier 2023A microstructure alcohol biosensor has been proposed to operate in the wavelength range of 0.8 to 2.0 μm for the
sensing of propanol, butanol, and pentanol, unveiling impressive results of relative sensitivity and confinement
loss. The results are achieved by implementing closely arranged cladding air holes of 3 rings with a single
elliptical core hole for analyte infiltration. Performance evaluation of the sensor was conducted using COMSOL
Multiphysics software and yields relative sensitivity of 96.75%, 89.60%, and 82.02% for propanol, butanol, and
pentanol, respectively, and confinement losses of 5.49 × 10 12 dB/m for propanol, 1.98 × 10 12 dB/m for
butanol, and 9.36 × 10 13 dB/m for pentanol. Other optical parameters have also been analysed that recorded
effective refractive index, high power fraction, low birefringence, small effective area, and large nonlinear co-
efficients. The proposed biosensor is eligible for practical application in alcohol sensing with these results.
Moreover, this proposed biosensor is suitable as a supercontinuum source in optical communication systems
because of the high nonlinear coefficients
Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling.
This research article published by MDPI, 2020Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (ES-DRL) algorithm in WSN. ES-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. ES-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, ES-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our ES-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods
Lifetime improved WSN using enhanced-LEACH and angle sector-based energy-aware TDMA scheduling
This research article published by Cogent Engineering, 2020Network lifetime remains as a significant requirement in Wireless Sensor
Network (WSN) exploited to prolong network processing. Deployment of low power
sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and
sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is nonoptimal in WSN which reflects in the drop of energy among sensor nodes. This paper
addresses the twofold as utilization of sensor nodes to prolong the node’s energy
and network lifetime by LEACH-based cluster formation and Time Division Multiple
Access scheduling (TDMA). Clusters are constructed by the design of an EnhancedLow-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel
operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm
Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values
estimation from GWO and D-PSO is concatenated to prefer the best optimal CH.
E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling
which segregates the coverage area into 24 sectors. Alternate sectors are assigne
Characteristics of Ultrasensitive Hexagonal-Cored Photonic Crystal Fiber for Hazardous Chemical Sensing
This research article was published by MDPI 2022A highly sensitive non-complex cored photonic crystal fiber sensor for hazardous chemical
sensing with water, ethanol, and benzene analytes has been proposed and is numerically analyzed
using a full-vector finite element method. The proposed fiber consists of a hexagonal core hole and
two cladding air hole rings, operating in the lower operating wavelength of 0.8 to 2.6 μm. It has been
shown that the structure has high relative sensitivity of 94.47% for water, 96.32% for ethanol and
99.63% for benzene, and low confinement losses of 7.31 × 10−9 dB/m for water, 3.70 × 10−10 dB/m
ethanol and 1.76 × 10−13 dB/m benzene. It also displays a high power fraction and almost flattened
chromatic dispersion. The results demonstrate the applicability of the proposed fiber design for
chemical sensing applications
Design and Simulation of Photonic Crystal Fiber for Liquid Sensing
This research article was published by MDPI 2021A simple hexagonal lattice photonic crystal fiber model with liquid-infiltrated core for
different liquids: water, ethanol and benzene, has been proposed. In the proposed structure, three
air hole rings are present in the cladding and three equal sized air holes are present in the core.
Numerical investigation of the proposed fiber has been performed using full vector finite element
method with anisotropic perfectly match layers, to show that the proposed simple structure exhibits
high relative sensitivity, high power fraction, relatively high birefringence, low chromatic dispersion,
low confinement loss, small effective area, and high nonlinear coefficient. All these properties have
been numerically investigated at a wider wavelength regime 0.6–1.8 μm within mostly the IR region.
Relative sensitivities of water, ethanol and benzene are obtained at 62.60%, 65.34% and 74.50%,
respectively, and the nonlinear coefficients are 69.4 W−1 km−1 for water, 73.8 W−1 km−1 for ethanol
and 95.4 W−1 km−1 for benzene, at 1.3 μm operating wavelength. The simple structure can be easily
fabricated for practical use, and assessment of its multiple waveguide properties has justified its
usage in real liquid detection
Repurposing Nonnucleoside Antivirals Against SARS-CoV2 NSP12 (RNA Dependent RNA Polymerase) and Identification of Domain Specific Interactions
The pandemic of SARS-CoV-2 has necessitated
expedited research efforts towards finding potential antiviral targets and drug
development measures. While new drug
discovery is time consuming, drug repurposing has been a promising area for
elaborate virtual screening and identification of existing FDA approved drugs
that could possibly be used for targeting against functions of various proteins
of SARS-CoV-2 virus. RNA dependent RNA polymerase (RdRp) is an important enzyme
for the virus that mediates replication of the viral RNA. Inhibition of RdRp could
inhibit viral RNA replication and thus new virus particle production. Here, we
screened non-nucleoside antivirals and found three out of them to be strongest
in binding to RdRp. We propose these three drugs as potential RdRp inhibitors
based on the site of binding. </p