3 research outputs found
DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
Influence Maximization is the task of selecting optimal nodes maximising the
influence spread in social networks. This study proposes a Discretized
Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion
in social networks. By discretizing meta-heuristic algorithms and infusing them
with quantum-inspired enhancements, we address issues like premature
convergence and low efficacy. The proposed method, guided by quantum
principles, offers a promising solution for Influence Maximisation. Experiments
on four real-world datasets reveal DQSSA's superior performance as compared to
established cutting-edge algorithms.Comment: AAAI Conference on Artificial Intelligence 202
Optimizing Electric Vehicle Efficiency with Real-Time Telemetry using Machine Learning
In the contemporary world with degrading natural resources, the urgency of
energy efficiency has become imperative due to the conservation and
environmental safeguarding. Therefore, it's crucial to look for advanced
technology to minimize energy consumption. This research focuses on the
optimization of battery-electric city style vehicles through the use of a
real-time in-car telemetry system that communicates between components through
the robust Controller Area Network (CAN) protocol. By harnessing real-time data
from various sensors embedded within vehicles, our driving assistance system
provides the driver with visual and haptic actionable feedback that guides the
driver on using the optimum driving style to minimize power consumed by the
vehicle. To develop the pace feedback mechanism for the driver, real-time data
is collected through a Shell Eco Marathon Urban Concept vehicle platform and
after pre-processing, it is analyzed using the novel machine learning algorithm
TEMSL, that outperforms the existing baseline approaches across various
performance metrics. This innovative method after numerous experimentation has
proven effective in enhancing energy efficiency, guiding the driver along the
track, and reducing human errors. The driving-assistance system offers a range
of utilities, from cost savings and extended vehicle lifespan to significant
contributions to environmental conservation and sustainable driving practices
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data