I'm using the cas-sc
class but I can't get it to print the authors' names, affiliations, abstract, and the keywords.
Here is my code:
\documentclass[a4paper,fleqn]{cas-sc}
\begin{document}
\title{A coordination mechnanism}
\author{Ghulam Hafeez$^{1,2*}$, Ammar Ali$^1$, Salman Ahmad$^4$, Nadeem Javaid$^{1}$, \\Muhammad Usman$^2$, and Khurram Saleem Alimgeer$^1$\\
$^{1}$COMSATS University Islamabad, Islamabad 44000, Pakistan\\
$^{2}$University of Engineering and Technology, Mardan 23200, Pakistan\\
%$^{3}$PMAS Agriculture University, Rawalpindi 46000, Pakistan\\
$^{3}$Wah Engineering College, University of Wah, Wah Cantt 47070, Pakistan\\
%$^{2}$CAMS, Dept of Biomedical Technology, KSU, Riyadh 11633, Saudi Arabia\\
%$^{3}$FE, Dalhousie University, Halifax, NS B3J 4R2, Canada\\
$^{*}$Corresponding author: ghulamhafeez393@gmail.com
}
\begin{abstract}[S U M M A R Y]
Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where demand response strategy is used to modify the nature of demand of consumer. In return, utility paid incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand in residential area and irregular electricity load profile have encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution {that shifts the electricity load} from On-peak to Off-peak hours. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load sifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. {Moreover, for} real-time rescheduling, concept of coordination among home appliances is presented. This helps {the scheduler} to optimally decide the ON/OFF status of appliances {to reduce the waiting time of the appliance.} Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. {Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user\textquotesingle s demand.} To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak average ratio and waiting time minimization. {Also, test functions for convex, non-convex and discontinuous Pareto front are studied to prove the efficacy of proposed techniques.}
\end{abstract}
\begin{keywords}
Coordination \sep Dynamic programming \sep Knapsack \sep Multi-objective optimization \sep Pareto front \sep Meta-heuristic \sep Nature-inspired \sep Bird swarm and Cuckoo search algorithm \sep Hybrid technique \sep Demand side management \sep Demand response \sep Smart grid.
\end{keywords}
\section{Intro}
Test
Test
Tes
\end{document}