Ando Ny

Aina Randriantsoa

Ali Hamada Fakra
email: fakra@univ-reunion.fr

Damien Ali

Hamada Fakra

Pierrot Manitra

Mohamed Ranjaranimaro

Mohamed Nasroudine

Jean Claude Rachadi

Gatina

Manitra Pierrot Ranjaranimaro

Mohamed Rachadi

Jean Claude Gatina

A new multiscale tool for simulating smart-grid energy management based on a systemic approach

Keywords: SMARTEN, energy management, energy-mix, smart grid, multiscale, simulation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Most activities today depend on energy and include transport, industry, housing, agriculture, air conditioning, and health. The most developed countries exhibit higher energy consumption [START_REF] Chen | Energy overview for globalized world economy: Source, supply chain and sink[END_REF]. However, global energy production is still dominated by fossil energy sources [START_REF] Martins | Fossil fuel energy consumption in european countries[END_REF], and the economy and the consumption of fossil energy of a country are still closely linked [START_REF] Mensah | Analysis on the nexus of economic growth, fossil fuel energy consumption, co2 emissions and oil price in africa based on a pmg panel ardl approach[END_REF]. Thus, the use of renewable energy sources is encouraged to address the problems of depletion and pollution of fossil energy sources. Nevertheless, renewable energy technologies [photovoltaic (PV) systems, wind turbine systems, etc.] still have the problem of intermittency [START_REF] Lehtola | Solar energy and wind power supply supported by storage technology: A review[END_REF]. Thus, to fully satisfy energy needs, an energy mix system is needed. Nonrenewable energy sources and renewable energy sources are mixed to satisfy the load. This energy mix has to be managed to optimize its use, and the current electrical grid is going to transition to a smart grid.

Smart grids are defined in many ways in the literature [START_REF] Dileep | A survey on smart grid technologies and applications[END_REF]. A smart grid is an electricity network that can intelligently integrate the actions of all users connected to it. This grid aims to deliver sustainable, economic, and secure electricity supplies. The latest technologies, computer science, tools and techniques will be taken into consideration to insert a form of intelligence into the electrical network [START_REF] Sanchez-Hidalgo | A survey on visual data representation for smart grids control and monitoring, Sustainable Energy[END_REF] to optimize the energy flows distribution and integrate renewable energy sources.

The protection of the smart grid and maintenance of the power quality are considerable challenges for the integration of renewable energy in a smart grid [START_REF] Mahela | Assessment of power quality in the utility grid integrated with wind energy generation[END_REF]. Some solutions were suggested in the literature, such as the use of the Stockwell transform method to identify islanding, outage of renewable energy source or other faults in the network [START_REF] Kaushik | Recognition of islanding and operational events in power system with renewable energy penetration using a stockwell transform-based method[END_REF]. In this study, the protection of the grid against outage and power quality will be mostly assumed by the integration of batteries in the circuit. The algorithms of the proposed tool will also allow this protection by different operating parameters. Beyond these challenges, the integration of renewable energy in the smart grid will generate benefits including :

• More efficient transmission of electricity • Quicker restoration of electricity after power disturbances

• Reduced operations and management costs for utilities, and ultimately lower power costs for consumers • Reduced peak demand, which will also help lower electricity rates

• Increased integration of large-scale renewable energy systems

• Better integration of customer-owner power generation systems including renewable energy systems

• Improved security

In this paper, a simulation tool called Smart Management AlgoRiThm of Electricity Network (SMARTEN) will be described to manage this smart grid environment. The energy from the current electrical grid will be mixed with renewable energy and energy from a storage system to fulfil the energy demands of the connected buildings.

The major contribution of this work is to propose two new strategies based on scenarios for simulating the management of energy flow in a smart grid. The algorithms of these strategies take into consideration the status of energy demand, renewable energy production, grid production, and storage batteries. The systemic approach used in this study has not been widely discussed in this field and can be considered a new and simple method to deal with energy flow management. The two-dimensional (2D) visualization proposed in this software is a tool that will help decision-makers observe every time step of the energy situation of each building in the connected grid.

This paper contains a bibliography of existing methods and tools; the operating principle of the new software, which includes a description of the two new algorithms based on scenarios; reliability tests of the software based on comparing validation elements with HOMER software; an example of an application that includes the input and output variables of the simulation and the parameters that the software needs to operate; and finally a general conclusion with ideas on how to improve the software.

State of the art

Optimization of the energy mix and management of the smart grid have been studied by several articles of research. In this section, state-of-the-art tools related to energy and energy-mix management will be described. The general uses of existing tools will be understood and the new tool developed in this study will be discussed.

Energy-mix optimization tools in buildings

There are studies focused on the relationship between the design of the building, energy mix associated with this building, and cost of energy (production and consumption). The most relevant papers in this field will be presented in the next several paragraphs. Fabrizio Ascione et al. [START_REF] Ascione | Multi-objective optimization of the renewable energy mix for a building[END_REF] conducted research about the optimization of the energy mix in a building. In their paper, the developed optimization procedure was based on a genetic algorithm and was performed by the use of EnergyPlus and MATLAB software. Many tools allow simulations to optimize the energy and performance related to a building. A review of the optimization of building-energy tools was performed by Anh-TuanNguye et al. [START_REF] Nguyen | A review on simulation-based optimization methods applied to building performance analysis[END_REF]; their paper shows that among these tools, the most used tool for building-energy simulation is EnergyPlus. Overall, 37.2% of major simulations about building optimization have been performed with this tool [START_REF] Nguyen | A review on simulation-based optimization methods applied to building performance analysis[END_REF]. In this paper, compared with EnergyPlus, the proposed tool will be able to simulate energy flows from a single building to several buildings. The energy mix can be managed not only for a building but for an entire town or an island application.

Hybrid system designing tools

Before conceiving the new tool, a review of available hybrid system designing tools is needed.

Many tools are available, so a comprehensive review is essential to understand their main features and choose the best types. Sinha and Chandel [START_REF] Sinha | Review of software tools for hybrid renewable energy systems[END_REF] presented a review of 19 software packages that help with the design, analysis, optimization, and economic viability of hybrid systems. A summary of these tools is shown in Table 1. There are also authors who proposed a classification of these tools to allow for a specific orientation. Dave Turcotte et al. [START_REF] Turcotte | PHOTOVOLTAIC HYBRID SYSTEM SIZING AND SIMULATION TOOLS: STATUS AND NEEDS, PV Horizon : Workshop on Photovoltaic Hybrid Systems[END_REF] proposed this classification: (1) Prefeasibility tools: mainly used to evaluate if there is global opportunity about implementing the system, this category of tool delivers a rough sizing analysis and often include a comprehensive cost and financial analysis (e.g., RETScreen); (2) Sizing tools: by performing a calculation of the system dimensions, users can insert an energy requirement, and the tool will determine the optimal size of each component related The time step of the simulation depends on the tool and the availability of input data (as weather data, for example), but simulations are commonly conducted on an hourly basis. Sizing operations can also be performed by simulation tools, but the user has to identify the key variables of the system, then run the simulation, adjust the variable, and finally repeat the simulation until the system meets an acceptable sizing.

Luis Arribas et al. [START_REF] Aarribas | World-wide overview of design and simulation tools for hybrid pv systems[END_REF] proposed another classification method for hybrid system tools. In their report, four categories were listed : The descriptions of these categories are the same as those of Dave Turcotte et al. [START_REF] Turcotte | PHOTOVOLTAIC HYBRID SYSTEM SIZING AND SIMULATION TOOLS: STATUS AND NEEDS, PV Horizon : Workshop on Photovoltaic Hybrid Systems[END_REF] except for the minigrid design tools. This category allows users to determine if a stand-alone renewable energy home system or a system connected to the centralized system is more cost-effective for a single house in a village. Commonly, tools in the minigrid design category allow the users to minimize power losses in the distribution network at a proper voltage level and an adequate section of distribution cables [START_REF] Aarribas | World-wide overview of design and simulation tools for hybrid pv systems[END_REF].

Renewable-energy-integration tools

Connolly et al. [START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF] performed a survey of 37 computer tools regarding the integration of energy systems. In that survey, three hybrid simulation tools (HOMER, RETScreen, and TRNSYS) were reviewed. The survey was carried out with the collaboration of tool developers, and the following topics were studied: background information, users, tool properties, applications, case studies, and further information. Connolly et al. [START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF] proposed seven tool types that can be used to describe an energy tool. According to the authors, an energy tool can be associated with one or more types.

These energy tool types are as follows:

• simulation tool, which simulates the operation of a system to supply the energy demand

• scenario tool, which usually combines a series of years into a long-term scenario

• equilibrium tool, which seeks to explain the behavior of supply, demand, and prices within a general or partial economy

• top-down tool, which uses general macroeconomic data to determine how energy prices and demands grow

• bottom-up tool, which identifies and analyzes specific energy technologies, investment options, and alternatives

• operation optimization tool, which optimizes the operation of a system; operation optimization tools are typically simulation tools

• investment optimization tool, which optimizes the investments in an energy system; investment optimization tools are typically scenario tools

The table 2 shows a summary of the 37 reviewed tools and indicates the type of each tool according to the classification of Connolly et al. [START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF].

- - Yes - - - - ProdRisk Yes - - - - Yes Yes RAMSES Yes - - - Yes Yes - RETScreen - Yes - - Yes - Yes SimREN - - - - - - - SIVAEL - - - - - - - STREAM Yes - - - - - - TRNSYS16 Yes Yes - - Yes Yes Yes UniSyD3.0 - Yes Yes - Yes - - WASP Yes - - - - - Yes WILMAR Planning Tool Yes - - - - Yes -
On the other hand, there is no energy tool that can address all issues about the integration 130 of renewable energy into the current electrical grid. Consequently, the choice of tool depends on the specific objectives of the final user. This indicates the importance of a review of existing tools about hybrid systems and renewable energy integration.

Sinha and Chandel [START_REF] Sinha | Review of software tools for hybrid renewable energy systems[END_REF] reported in a review of 19 tools of hybrid renewable energy systems that "HOMER is found to be most widely used tool as it has maximum combination of renewable 135 energy systems and performs optimization and sensitivity analysis which makes it easier and faster to evaluate the many possible system configurations." Through HOMER, Rajbongshi et al. [START_REF] Rajbongshi | Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER[END_REF] conducted an optimization of hybrid systems applied to rural electrification. HOMER has over 200,000 users around the world and is the global standard tool for microgrid and distributed energy system design [START_REF]HOMER -hybrid renewable and distributed generation system design software[END_REF], [START_REF] Juneja | Design of smart grid in rural areas using homer[END_REF], [START_REF] Garni | Optimal design and analysis of grid-connected photovoltaic under different tracking systems using homer[END_REF]. In this study, a validation of SMARTEN will be conducted via a comparison of the results with HOMER. section is considered by SMARTEN, while the lower section consists of all subsystems related to the upper section and is not considered by SMARTEN.

The grid system (RES) used in SMARTEN does not include the process by which the electrical power is made available. There are several energy sources to feed the grid, and each country has its specific energy sources for that purpose. Chen and Wu [START_REF] Chen | Energy overview for globalized world economy: Source, supply chain and sink[END_REF] performed an overview of sources of energy around the world. To run a simulation in SMARTEN, the grid system is represented by the energy delivered by the grid that is available for the end customers.

For a renewable energy production system (SP), the energy sources can be from hydropower, solar energy (PV, thermal), biomass, wind energy, geothermal energy, or any renewable energy source. The renewable energy sources among the world and their uses were reported by REN21 [START_REF] Environment | Renewables 2019 global status report[END_REF]. In this study, each building could provide renewable energy from any hybrid system. Each building considered in the simulation was also connected to the grid. Therefore, to run a simulation in SMARTEN, the RE (Renewable Energy) production system was represented by the renewable energy available generated by each building.

Each building simulated in SMARTEN also has an energy demand (SC). Our tool takes into account 4 types of consumption profile: residential, industrial, community, and commercial. Thus, to run a simulation in SMARTEN, the consumption system is represented by the electrical power need for each building for each hour.

The storage system (STO) includes technologies and transformations for storing electrical energy. Research studies have been conducted in order to optimize and integrate storage systems into the grid [START_REF] Kerdphol | Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids[END_REF], [START_REF] Christakou | A unified control strategy for active distribution networks via demand response and distributed energy storage systems, Sustainable Energy[END_REF]. Palizban et al. also described the characteristics of energy storage applied in modern grids [START_REF] Palizban | Energy storage systems in modern grids-matrix of technologies and applications[END_REF]. Thus, to run a simulation in SMARTEN, the storage system is represented by the energy available from the storage system (extraction process) and attributed to the storage system (charging process). As parameters, the higher and lower limits of the storage system are also considered. The converting process is taken into consideration in one of the two strategies in SMARTEN. In the development of our tool, we started with a battery storage model and intend to integrate other types of energy storage models. The main objective of strategy 1 is to minimize consumption peaks and smooth the energy consumption curve. For this purpose, when the consumption is close to a defined limit, energy is extracted from storage systems to avoid excessive extraction from the grid. Thus, priority is given to storage systems charging, and strategy 1 saves energy costs from the grid extraction. Table 3 shows data related to strategy 1.

RE production system (SP)

Consumption system (SC)

Storage system (STO)

Grid system (RES) The algorithm of strategy 1 deals with the production system and the consumption system to decide how the energy should be extracted from the electrical network or from the storage system and verify if there is any excess energy production. There are two main scenario cases:

• Case 1: The demand is less than or equal to the withdrawal limit at the network level

• Case 2: The demand is higher than the withdrawal limit at the network level These two main cases can be split into several subcases in order to refine the management of energy flows. The simulation depends on the imported parameters. If there is no RE production, then energy will be completely drawn from the grid. In strategy 1, there are 12 scenarios according to four parameters of numerical simulation. Energy demand will be met by the storage system until that storage system reaches the lower capacity limit. However, if the energy from the storage system is insufficient, then the rest will be drawn beyond the grid extraction limit from the grid system. If there is sufficient RE production to support the excess consumption not realized from the grid, then part of the energy demand can be ensured by this

j = j + 1 j = 1 i = i + 1 Start j ? n i=1 Y N Y N End i ? m RES(i,j), STO(i,j), EXC(i,j) Calculation subroutine
Write RES(i,j), STO(i,j), EXC(i,j)

Read m, n production. The consumption at the grid level becomes 0 when the production makes it possible at the same time to charge the storage system and to fully ensure consumption. There will even be excess energy in the best production condition.

Algorithm of strategy 2 description

Strategy 2 aims to control energy flows and maximize the use of renewable energy sources.

Energy demand is first met by renewable energy sources when they are available, and therefore the storage system is only recharged when the energy demand is satisfied. Table 4 shows data related to strategy 2. The algorithm of strategy 2 deals with the production system and the consumption system as strategy 1 does, but this algorithm also takes into account the battery maximum charging power and the battery maximum discharging power to decide how the energy should be extracted from the electrical network or from the storage system and verify if there is any excess energy production. The simulation depends on the same imported parameters as strategy 1, but there are five more parameters: electrical grid efficiency, storage battery efficiency, converter efficiency, maximum output power of the converter, and maximum input power of the converter. The logic approach of strategy 2 is based on the RE production, and there are three main cases:

• Case 1: there is no RE production

• Case 2: the RE production can fulfill the energy demand

j = j + 1 j = 1 i = i + 1 Start j ? n i=1 Y N Y N End i ? m
RES(i,j), STO(i,j), EXC1(i,j),EXC2(i,j) Calculation subroutine Write RES(i,j), STO(i,j), EXC1(i,j),EXC2(i,j)

Read m, n If there is no RE production, then the algorithm checks if the energy consumption is less than the grid withdrawal limit. Thus, the demand is satisfied entirely by the electrical network. Beyond this limit, the rest of the energy demand is satisfied by the storage system. The extraction from this storage system depends on operating parameters of the converter and the storage device. The algorithm then extracts the energy from the storage system until its lower capacity limit is reached If there is RE production, then the energy demand is fulfilled by the RE production, and the excess production will affect the charging storage device. This charging process continues until the higher capacity limit of the storage device is reached. If the consumption exceeds the converter maximum output power, then the rest of the energy demand is satisfied from the grid.

If RE production is lower than the energy demand, then the algorithm extracts energy from the electrical grid. However, if the energy extracted from the grid exceeds the grid withdrawal limit, then the algorithm will verify the situation of the storage system.

To summarize, The management algorithm extracts first from the RE production, then from the electrical grid, and finally from the storage system. This storage system will only be charged if the energy demand is met. There are 20 scenarios according to strategy 2. SP(i,j),SC(i,j), P_max_charge(i,j), P_max_decharge(i,j), rend_r(j), rend_bat(j), rend_ond(j), ond_out_max(j), ond_in_max(j), LH(j), LB(j), LR(j), STOin(j) Thus, HOMER PRO 3.11.5 software was selected to perform this comparison. HOMER (Hybrid Optimization of Multiple Energy Resources) was developed at the National Renewable Energy Laboratory (NREL/USA, United States of America) [START_REF]HOMER -hybrid renewable and distributed generation system design software[END_REF]. This software can be used for standalone systems or microgrid-connected systems. In addition, HOMER is a tool used to assist in the design, sizing, optimization, and simulation of hybrid renewable energy systems and is widely used by researchers to conduct simulations on new configurations of renewable energy systems [START_REF] Zahboune | Optimal hybrid renewable energy design in autonomous system using modified electric system cascade analysis and homer software[END_REF], [START_REF] Kim | The case study of combined cooling heat and power and photovoltaic systems for building customers using homer software[END_REF]. HOMER offers different configurations to users depending on their purpose. It can be used to identify the least expensive options. Among the features of HOMER is support of the hourly profile of the electrical consumption.

RES = SC STO = STO(i-1) EXC1= 0 EXC2 = 0 RES = LR STO = LB EXC1= 0 EXC2 = 0 RES = SC STO = STO(i-1) -ond_in_max/rend_bat EXC1= 0 EXC2 = 0 RES = LR STO = STO(i-1) -SC -LR/rend_ond*rend_bat EXC1= 0 EXC2 = 0 RES = LR STO = STO(i-1) -SC -LR/rend_ond*rend_bat EXC1= 0 EXC2 = 0 A D FOR ALL TEST RIGHT IS YES LEFT IS NO
Two main strategies govern the optimal management of energy fluxes by the software. We have

• Following Load: the strategy consists of meeting demand by following the load at all times from the renewable energy source first, then from the grid if the renewable energy production is not sufficient. In this strategy, batteries are charged only by photovoltaic energy production during low energy demand.

• Cycle Charging: the energy demand is met by the grid until a defined level of power. Beyond this level, the batteries supply the energy. The generator's (i.e., photovoltaic system) excess production is used to recharge the batteries first. If renewable energy is not available, then the batteries are recharged by the grid.

The mathematical modeling of HOMER is also described in Table 5 to allow for a better understanding of the selected software.

P P V = Y P V f P V ( G T G T ,ST C
) ).P W T G,ST P

Where:

• Y P V = rated capacity of the PV array, meaning its power output under standard test conditions [kW] • η hyd = hydroturbine efficiency [%]

• f P V = PV derating factor [%] • G T =
• ρ water = density of water [1000 kg.m -3 ]

• g = acceleration due to gravity [9.81 m.s -2 ]

• h net = effective head [m] • Q turbine = hydroturbine flow rate [m 3 .s -1 ]
• H ave = monthly average radiation on the horizontal surface of the earth [kW h.m -2 .day -1 ]

• H o,ave = extraterrestrial horizontal radiation, meaning the radiation on a horizontal surface at the top of the earth's atmosphere [kW h.m -2 .day -1 ]

• Q 1 = available energy [kWh] in storage at the beginning of the time step

• Q = total amount of energy [kWh] in storage at the beginning of the time step

• c = storage capacity ratio [unitless] • k = storage rate constant [h -1 ]
• ∆t = length of the time step [h]

• Q max = total capacity [kWh] of the storage bank

• P W T G = wind turbine power output [kW]

• P W T G,ST P = wind turbine power output at standard temperature and pressure [kW]

• ρ = actual air density [kg.m -3 ]

• ρ 0 = air density at standard temperature and pressure (1.225 kg.m -3 )

In SMARTEN, a systemic approach is used to manage a smart grid. The behavior of the proposed tool depends on several conditions and parameters, and the production and consumption data are imported as .xlsx files. The calculation of power outputs of renewable energy sources is not included in SMARTEN. These data are imported from existing studies. The proposed tool does not take into consideration these subsystems of production as seen in Figure 1. The algorithms of the two strategies of SMARTEN focus only on the management of the energy flow. The energy flow management of the proposed software is shown in Figure 9. The parameters of the simulation are given in Table 6, and the grid system efficiency is supposed to be 0.80. The parameters are as follows: ηbat refers to the storage battery efficiency, ηconv refers to the converter efficiency, Max conv. is the output for the maximum output power of the converter, Max conv. is the input for the maximum input power of the converter, LR is the grid withdrawal limit, LH is the upper limit of storage capacity, LB is the lower limit of the storage capacity, and STOi-1 is the storage situation for the previous time step. The values of the efficiency of the batteries and converter are needed for the comparison, and the Following Load strategy of HOMER is closer to strategy 2 of SMARTEN. Thus, strategy 2 of SMARTEN was used for this comparison.

Results of the reliability test

An advantage of SMARTEN compared to HOMER is that the simulation for all four buildings is made simultaneously, while on HOMER, the simulation is carried out building by building. This is the multiscale aspect of SMARTEN. Thus, this multiscale aspect allows the distribution of energy-mix flows into connected buildings of the simulated environment.

Figure 10 shows the variation of the storage system for a year of simulation for Residential Building 1. On the second y-axis, it shows the relative error between SMARTEN and HOMER for that variation. As seen in Figure 10, where the maximum relative error about storage system is found during the entire simulation, the difference between SMARTEN and HOMER is very close. The maximum relative error is observed on July 7, 2007, at 19:00. It corresponds to a difference of power approximately 5.34 W, which refers to a 2.4x10 -4 % relative error. This result is due to higher energy demand met by the storage system and emphasizes the slight difference between the two tools.

Figure 11 shows the variation of the grid extraction from January 1 at 00:00 to January 20, 2007, at 23:00 for the Commercial Building. The second y-axis shows the relative error between SMARTEN and HOMER for that variation. This range of time is chosen for Figure 11 Figure 11 shows that general management simulation proposed in SMARTEN algorithms (particularly with strategy 2) is very similar to the management of the HOMER software. The maximum error between SMARTEN and HOMER corresponds to a difference of power of approximately 45.2 W. Thus, the maximum relative error value is 4.78%. For that Commercial Building, the grid withdrawal limit is approximately 255 kWh. As a result, the power difference compared to the grid withdrawal limit is negligible (1.77x10 -2 %). The differences between residential and commercial buildings are the capacity of the storage system, the proprieties of the converter, and the grid withdrawal limit. These parameters contribute to a higher relative error for simulating commercial buildings compared to residential buildings.

Finally, the maximum error values for the simulations of these four buildings are summarized in Table 7. Very small errors of approximately 2x10 -4 % are found for storage system management and errors not exceeding 5 % for grid extraction management. These comparison results indicate the reliability degree of SMARTEN to manage the energy mix in a smart grid environment. Comparison between HOMER and SMARTEN summarized in Table 8 according to classification method of Connelly et al. [START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF] with additional specifications related to this study. The main difference between SMARTEN and HOMER is the conception method. SMARTEN uses a systemic approach while HOMER uses mathematical modeling. Another difference about running simulations is the multiscale aspect. Simulating several buildings is laborious in HOMER because each building is simulated one by one. In SMARTEN, the simulation of these four buildings is conducted simultaneously. Savings in time and labor is therefore observed when using SMARTEN. Furthermore, the possibility of 2D visualization of all buildings simulated in a territory makes SMARTEN more efficient about time and space management. This aspect is detailed further in paragraph 5.3.2.

Example of application

Objective of application

After validating the reliability of SMARTEN in comparison to HOMER, an example of application is then presented in this section. The main objective of this example of application is to demonstrate the main features of the proposed tool in the management of energy flow. The proposed tool will be applied in a single building and then in a smart grid composed of 20 interconnected buildings to understand the impact of the 2D visualization in decision-making.

Hypothesis of application

The algorithms of strategy 1 and strategy 2 are inserted in a graphical user interface (GUI).

The GUI was designed in a 64-bit version of Windows and the MATLAB 2015b release in order to allow for fluid calculation of larger matrices.

Hypothesis of single-building application

For this application of SMARTEN in a single building, the renewable energy source (PV) is a set of solar panels, and the storage system (STO) consists of batteries. The profile of production of renewable energy was provided by the NASA annual database in Colorado, USA, in 2007. Two dates were chosen to determine the impact of the use of SMARTEN: June 21 (see Figure 12), which corresponds to the summer solstice; and December 21 (see Figure 13), which corresponds to the winter solstice in the Northern Hemisphere. Strategy 1 of SMARTEN is selected to smooth the consumption and reduce the energy cost. The algorithms of this strategy will smooth the energy consumption by using storage units to supply the energy demand beyond a defined limit to avoid excessive costs of energy.

The grid system efficiency is supposed to be 0.80, and the other parameters used in this application are set by a typical commercial building load profile (see Table 9).

Hypothesis of microgrid application

In this example of application in a smart grid, the renewable energy source (PV) is also considered a set of solar panels, and the storage system (STO) consists of batteries. The profile of production of renewable energy was provided by the NASA annual database in Colorado, USA, for January 1, 2007. For this application, strategy 2 of SMARTEN was selected to maximize the use of renewable energy to meet the energy demand extraction. The algorithms of this strategy will smooth the energy consumption by using the renewable energy sources and storage units to supply the energy demand. This management strategy will minimize the extraction from the grid.

As with the previous hypothesis, the grid system efficiency is supposed to be 0.80, and the other parameters used in this application are set by a random distribution of 20 residential buildings' typical load profiles.

Results of application and interpretation

Results of single-building application

For the single-building application, the results paragraph will focus on the impacts of SMARTEN in the management of energy mix and energy cost savings.

On June 21 (see Figure 12), the sun rose at around 6:00 a.m. The first part of the graphic shows that there is no renewable energy production between 12:00 a.m and 6:00 a.m. The energy demand was supplied by the grid, so the consumption and grid withdrawal curves are mixed. From 6:00 a.m to 18:00 p.m, the photovoltaic panels produced energy, and most of the energy demand was supplied by the renewable energy. Thus, the grid withdrawal curve was under the consumption curve due to renewable energy production. Beyond 18:00 p.m, there was no renewable energy production, and the energy demand was entirely supplied by the grid. A peak of consumption of 334.8 kW was detected at 11:00 a.m. The grid extraction when using SMARTEN is limited to 255 kW according to the parameter LR (grid withdrawal limit), and a 23.83% of energy costs were saved by the proposed tool. The maximum energy saved was realized at 13:00 p.m when the photovoltaic panels produced the highest energy. At this time, 66.30% of the energy costs were saved using SMARTEN.

On December 21 (see Figure 13), the sun rose at around 5:00 a.m. Consequently, there was no renewable energy production from 12:00 a.m to 5:00 a.m., and hence, the consumption and the grid withdrawal curves are mixed. A difference between the consumption and the grid withdrawal curves was observed at 5:00. The photovoltaic panels continued to produce energy until 18:00.

After this time, the energy demand was entirely supplied by the grid. The peak of consumption of approximately 225.73 kW was detected at 9:00 a.m, but only 111.79 kW was extracted from the grid, so 50.47% of the energy cost was saved by the use of the proposed software. On this day, when the renewable energy source production was optimum from 11:00 a.m to 13:00 a.m, the energy demand was entirely supplied by the renewable energy source. During this time gap, 100% of the energy cost could be saved by the use of SMARTEN.

The application of SMARTEN in a commercial building can allow users to save approximately 23.83% of the energy cost during higher peaks of consumption. Under certain conditions, and when the renewable energy source production is at its highest, 100% of the energy cost can be saved.

The integration of SMARTEN in the smart grid will save on energy costs and reduce the use of fossil energy, which is the main energy source of the current grid.

Results of smart grid application

For a smart grid application, the results paragraph will focus on the interface of SMARTEN and the impact of its 2D visualization in the management of energy flow in a smart grid.

SMARTEN has a very user-friendly interface. The user proceeds with the parameterization of the simulation by following the indications on this interface, such as the choice of the desired strategy and the import of data. A graph (see Figure 14) is also available in the interface to allow users to see the evolution of all data through time for each building. The ID (Identity Document) number of the 20 interconnected buildings and the display time (in hours) are editable in the graphics panel [START_REF] Chen | Energy overview for globalized world economy: Source, supply chain and sink[END_REF]. Thus, the user can display the energy curves of a specific building separately.

A legend [START_REF] Martins | Fossil fuel energy consumption in european countries[END_REF] indicates each plotted data item by color, and zooming can be performed with the scroll of a computer mouse. In this example, Residential Building 2 has a consumption peak at 07:00 p.m. The algorithms of SMARTEN detect that the consumption is higher than the Network Withdrawal Limit (LR), and the excessive consumption is fulfilled by the storage batteries because there is no renewable energy production. Through this 2D visualization, it is possible to answer the following questions: which building has the most important energy consumption, which building has the maximum renewable energy production, the location and time of the highest renewable demand, where to establish a new renewable energy system, and where to install the storage system. In this example, the residential building at the west side of the village has the highest consumption (yellow). Any questions about "where" and "when" related to the considered environment can be resolved by the use of the 2D visualization of SMARTEN. As a result, users can make a decision by taking into consideration the location of a specified building and the data related to it for every time step.

Conclusions and perspectives

The depletion and pollution issues of fossil energy sources and growth in global energy demand are the main reasons to use renewable energy sources. To compensate for the intermittency of these renewable energy sources, we use storage systems and mix them with the energy from the current grid system. A smart grid is thus implemented and has to be managed.

A new tool called Smart Management AlgoRiThm of Electricity Network (SMARTEN) was proposed. The software considers the renewable energy production and consumption systems to decide how the energy is extracted from the current electrical grid, from renewable energy sources, or from the storage system through different parameters. Two new strategies are available. Strategy 1 smoothes the consumption curve and avoids excessive energy costs, while strategy 2 maximizes the use of renewable energy and preserves the environment.

Reliability tests were conducted by comparison with HOMER software. For the storage system, a maximum relative error of approximately 2.4x10 -4 % was observed. For extraction from the electrical grid, a maximum relative error of approximately 4.78% was observed.

However, SMARTEN can simultaneously perform the simulation of several buildings when HOMER simulates only one at a time. In addition, SMARTEN can provide a 2D visualization of the area where the buildings are located. SMARTEN helps decision-makers determine the exact position of the future implementation of renewable energy systems: where to install new storage systems, renewable energy sources, the location and orientation of wind energy sources, the available space for a photovoltaic field, etc.

Further research to improve the software will be conducted by adding environmental and financial reports and new strategies. These are necessary to complete future releases of SMARTEN because it aims to be a decision-support and research tool.

  to the system; the energy flows among components and some indications about the critical periods of the year are provided by most of the sizing tools (e.g., HOMER); (3) Simulation tools: the user has to specify the nature and size of each component, and an analysis of the behavior of the system is then provided by the tool (e.g., HYBRID 2); (4) Open architecture research tools: traditional simulation tools do not allow users to modify the algorithms nor the behavior nor interactions of the individual components, while in an open architecture research tool, the modification of existing routines and the adding of new routines are allowed (e.g., TRNSYS). Researchers choose this tool category because of its flexibility and powerful application.

( 1 )

 1 Dimensioning: corresponds to the sizing tools of Dave Turcotte et al. [12] classification; (2) Simulation: corresponds to the simulation tools of Dave Turcotte et al. [12] classification; (3) Research: corresponds to the open-architecture research tools of Dave Turcotte et al. [12] and their classifications; (4) Minigrid design tools: assist in the design of a minigrid electrical distribution network.

3. Operating principle of SMARTEN 3 . 1 .Figure 1 :

 311 Figure 1: Relation between systems and SMARTEN

3. 2 .

 2 Algorithms of strategy 1 description SMARTEN has two control strategies depending on the final objective of the user. Strategy 1 aims to minimize consumption peaks and smooth the energy consumption curve. Strategy 2 aims to maximize the use of renewable energy and preserve environment. SMARTEN is built under an algorithmic logic made up of different scenarios. Input data are modeled in a matrix format, and the calculation process of SMARTEN was developed in the MATLAB environment. This MATLAB environment is more efficient and widely used for matrix calculation. Therefore, the input data are represented by a m × n matrix where m are the rows that represent the time step (an hour) of the simulation, and n are columns that represent the buildings connected to the considered environment. The management of energy distribution in a built space consists of knowing the situation of the different systems for each hour. SMARTEN can work on an hourly basis and interacts with four distinct systems [see Figure 2].

Figure 2 :

 2 Figure 2: Relations between systems

  Figure 3 and 4 show a flowchart of strategy 1 and a flowchart of the calculation subroutine. These flowcharts determine how SMARTEN manages the energy flow according to each scenario.

Figure 3 :Figure 4 :

 34 Figure 3: Flowchart of strategy 1

Figures 5, 6 , 7 ,

 67 and 8 show a flowchart of strategy 2 and a flowchart of the calculation subroutine. These flowcharts determine how SMARTEN manages the energy flow according to each scenario.

Figure 5 : 2 •

 52 Figure 5: Flowchart of strategy 2

  -1) -LB*rend_bat > SC -LR/rend_ond SC -LR/rend_ond*rend_bat < P_max_decharge Y N

Figure 6 :

 6 Figure 6: Flowchart of strategy 2 calculation subroutine -Part 1

Figure 7 :Figure 8 : 4 . 4 . 1 .

 78441 Figure 7: Flowchart of strategy 2 calculation subroutine -Part 2

  [1 + α P (Tc -T c,ST C )] Hydro Power Output P hyd = η hyd .ρ water .g.h net .Q turbine 1000Wbatt,cmax,kbm = kQ 1 e -k∆t +Qkc(1-e -k∆t )1-e -k∆t +c(k∆t-1+e -k∆t )Maximum BatteryDischarge Power P batt,dmax,kbm = -kcQmaxe -k∆t +kQ 1 e -k∆t +Qkc(1-e -k∆t ) 1-e -k∆t +c(k∆t-1+e -k∆t )

  solar radiation incident on the PV array in the current time step [kW.m -2 ] • G T,ST C = incident radiation at standard test conditions [1 kW.m -2 ] • α P = temperature coefficient of power [ °C] • T c = PV cell temperature in the current time step [ °C] • T c,ST C = PV cell temperature under standard test conditions [25 °C] • P hyd = power output of the hydroturbine [kW]

Figure 9 :

 9 Figure 9: Energy flow management system

Figure 10 :

 10 Figure 10: Storage system variation and relative error for Residential Building 1

  to allow better visualization of the maximum error in the variation of the grid extraction.

15 Figure 11 :

 1511 Figure 11: Grid extraction variation and relative error for Commercial Building

Figure 12 :

 12 Figure 12: Impact of SMARTEN for commercial building consumption on June 21, 2007

Figure 13 :

 13 Figure 13: Impact of SMARTEN for commercial building consumption on December 21, 2007

Figure 14 :

 14 Figure 14: Graphic display of SMARTEN

Figure 15 :

 15 Figure 15: Two-dimensional visualization on SMARTEN

Table 1 :

 1 Capabilities analysis of hybrid-system software tools[START_REF] Sinha | Review of software tools for hybrid renewable energy systems[END_REF]

	Tools	Economical Analysis Technical Analysis PV System Wind System Generator set Storage device Bio-energy Hydro energy Thermal System
	ARES-I &II	-	Yes	Yes	Yes	Yes	Yes	-	-	-
	Dymola/Modelica	Yes	-	Yes	Yes		Yes
	HOMER	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	-
	Hybrid Designer	Yes	-	Yes	Yes	Yes	Yes	-	-	-
	HYBRID2	-	Yes	Yes	Yes	Yes	Yes	-	-	Yes
	HYBRIDS	-	Yes	Yes	-	-	Yes	-	-	-
	HybSim	Yes	Yes	Yes	-	Yes	Yes	-	-	-
	HySim	Yes	Yes	Yes	-	Yes	Yes	-	-	-
	HYSYS	-	Yes	Yes	Yes	Yes	Yes	-	-	-
	iHOGA	Yes	Yes	Yes	Yes	Yes	Yes	-	Yes	-
	INSEL	-	Yes	Yes	Yes	Yes	Yes	-	-	Yes
	IPSYS	-	Yes	Yes	Yes	Yes	Yes	-	Yes	-
	RAPSIM	-	Yes	Yes	Yes	Yes	Yes	-	-	-
	RETScreen	Yes	Yes	Yes	Yes	-	Yes	-	-	-
	SOLSIM	Yes	Yes	Yes	Yes	Yes	Yes	Yes	-	-
	SOLSIM	Yes	Yes	Yes	Yes	Yes	Yes	Yes	-	-
	SOLSTOR	Yes	Yes	Yes	Yes	Yes	-	-	-	-
	SOMES	Yes	Yes	Yes	Yes	-	Yes	-	-	-
	TRNSYS	Yes	Yes	Yes	Yes	Yes	Yes	-	-	Yes
	iGRHYSO	Yes	Yes	Yes	Yes	-	Yes	-	Yes	-

Table 2 :

 2 Type of each renewable-energy-integration tool reviewed[START_REF] Connolly | A review of computer tools for analysing the integration of renewable energy into various energy systems[END_REF]

	Tool	Simulation	Scenario	Equilibrium	Top-down	Bottom-up	Operation	Investment
							optimization	optimization
	AEOLIUS	Yes	-	-	-	Yes	-	-
	BALMOREL	Yes	Yes	Partial	-	Yes	Yes	Yes
	BCHP Screening	Yes	-	-	-	Yes	Yes	-
	Tool
	COMPOSE	-	-	-	-	Yes	Yes	Yes
	E4cast	-	Yes	Yes	-	Yes	-	Yes
	EMCAS	Yes	Yes	-	-	Yes	-	Yes
	EMINENT	-	Yes	-	-	Yes	-	-
	EMPS	-	-	-	-	-	Yes	-
	EnergyPLAN	Yes	Yes	-	-	Yes	Yes	Yes
	energyPRO	Yes	Yes	-	-	-	Yes	Yes
	ENPEP-BALANCE	-	Yes	Yes	Yes	-	-	-
	GTMax	Yes	-	-	-	-	Yes	-
	H2RES	Yes	Yes	-	-	Yes	Yes	-
	HOMER	Yes	-	-	-	Yes	Yes	Yes
	HYDROGEMS	-	Yes	-	-	-	-	-
	IKARUS	-	Yes	-	-	Yes	-	Yes
	INFORSE	-	Yes	-	-	-	-	-
	Invert	Yes	Yes	-	-	Yes	-	Yes
	LEAP	Yes	Yes	-	Yes	Yes	-	-
	MARKAL/TIMES	-	Yes	Yes	Partly	Yes	-	Yes
	Mesap PlaNet	-	Yes	-	-	Yes	-	-
	MESSAGE	-	Yes	Partial	-	Yes	Yes	Yes
	MiniCAM	Yes	Yes	Partial	Yes	Yes	-	-
	NEMS	-	Yes	Yes	-	-	-	-
	ORCED	Yes	Yes	Yes	-	Yes	Yes	Yes
	PERSEUS	-	Yes	Yes	-	Yes	-	Yes
	PRIMES

Table 3 :

 3 List of data needed in strategy 1

		SP	renewable energy production system
	Input data
		SC	consumption system
		LR	grid withdrawal limit
		LH	upper limit of storage capacity
	Parameters
		LB	lower limit of the storage capacity
		STOi-1	storage situation for the previous time step
		RES	grid system extraction
	Output data	STO	storage system extraction
		EXC	excess energy production

Table 4 :

 4 List of data needed in strategy 2

		SP	renewable energy production system
		SC	consumption system
	Input data
		P ch	battery maximum charging power
		P dech	battery maximum discharging power
		rend r	electrical grid efficiency
		rend bat	storage battery efficiency
		rend ond	converter efficiency
		ond out max	maximum output power of the converter
	Parameters	ond in max	maximum input power of the converter
		LR	grid withdrawal limit
		LH	upper limit of storage capacity
		LB	lower limit of the storage capacity
		STOi-1	storage situation for the previous time step
		RES	grid system extraction
		STO	storage system extraction
	Output data
		EXC1	DC excess renewable-energy-production before the converter
		EXC2	AC excess renewable-energy-production after the converter

Table 5 :

 5 Mathematical Modeling of HOMER[START_REF]user manuel[END_REF]

	HOMER	Equation
	Calculation
	PV Array Output
	Power

Table 6 :

 6 Parameters of simulation in SMARTEN

	Building	ηbat	ηconv	Max conv.	Max conv.	LR	LH	LB	STOi-1
				output (kW)	input (kW)	(kWh)	(kWh)	(kWh)	(kWh)
	Residential 1	0.89	0.95	1.22	1.29	1.50	5.00	2.00	5.00
	Residential 2	0.89	0.95	0.48	0.51	1.50	2.00	0.80	2.00
	Commercial	0.89	0.95	137.72	144.97	255.00	379.30	151.72	379.30
	Residential 4	0.89	0.95	2.59	2.72	1.50	11.01	4.40	11.01

Table 7 :

 7 Relative errors committed by SMARTEN compared to HOMER software simulation for management ofThe SMARTEN reliability tests are essential for its implementation. The purpose of these reliability tests by comparison with the HOMER software is to check whether the results found in SMARTEN correspond to the results found in software widely used by researchers across the world under same hypothesis and parameters. During the simulation of the energy-mix management of four buildings, results obtained by SMARTEN differ from HOMER by approximately 2x10 -4 % for power extracted from storage batteries and by approximately 4.78% for power withdrawn from the grid. This reliability test, by comparison, shows that results obtained by SMARTEN agree closely with those obtained by HOMER software both for storage system management and grid extraction management.

	energy mix in a building
		Relative error (%)
	Buildings	Grid extraction	Storage system
	Residential 1	4.73 × 10 -2	2.40 × 10 -4
	Residential 2	1.39	1.50 × 10 -4
	Commercial	4.78	1.60 × 10 -4
	Residential 3	2.54 × 10 -2	8.00 × 10 -5
	4.3. Conclusion of reliability tests

Table 8 :

 8 Comparison between SMARTEN and HOMER

		SMARTEN	HOMER
	Simulation	Yes	Yes
	Scenario	-	-
	Equilibrium	-	-
	Top-down	-	-
	Bottom-up	-	Yes
	Operation Optimization	Yes	Yes
	Investment Optimization	-	Yes
	Conception Method	Systemic Approach	Mathematical Modeling
	Multiscale simulation	Yes	-
	Multiscale 2D Visualization	Yes	-

Table 9 :

 9 Parameters of commercial building application

	Building	ηbat	ηconv	Max conv.	Max conv.	LR	LH	LB	STOi-1
				output (kW)	input (kW)	(kWh)	(kWh)	(kWh)	(kWh)
	Commercial	0.89	0.95	137.72	144.97	255.00	379.30	151.72	379.30

Appendix A: Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix B: Acknowledgment

This work was developed with the collaboration of the Higher Institute of Technology of Antananarivo (I.S.T.), Ministry of Higher Education and Scientific Research, Iadiambola Ampasampito, PO Box 8122, Antananarivo 101, Madagascar; and the PIMENT Laboratory, University of la Réunion, 117 rue du General Ailleret -97430 Le Tampon -REUNION; and was funded by the ERASMUS+ project and "La Région Réunion."


Farman Ullah
email: farmanullah@cau.edu.cn

Hina Gul

Kaleem Tariq

Nicolas Desneux

Xiwu Gao

Dunlun Song
email: songdl@cau.edu.cn

Fitness costs in clothianidin-resistant population of the melon aphid, Aphis gossypii

Clothianidin is a second-generation neonicotinoid insecticide, widely used against sap-sucking insect pest including melon aphid, Aphis gossypii Glover (Hemiptera: Aphididae). This pest causes severe economic damage to Cucurbitaceae plants worldwide. In this study, we investigated clothianidin resistance development under continuous selection pressure. Moreover, the age-stage, two-sex life table approach was used to evaluate the impact of clothianidin resistance on the fitness of A. gossypii. A clothianidin resistant strain (CT-R) with a 23.17-fold resistance level was developed from a susceptible strain (CT-S) after continuous selection for 24 generations. Life table results showed a significant reduction in the relative fitness (0.847) of CT-R strain compared to the CT-S strain of A. gossypii. The developmental duration, oviposition days, total pre-oviposition period (TPOP), longevity, and fecundity of CT-R strain were found to be significantly lower when compared to CT-S strain. The demographic parameters, including the intrinsic rate of increase (r), finite rate of increase (λ), net reproductive rate (R 0 ), and mean generation time (T) were also significantly decreased in CT-R strain compared to the CT-S strain. Both the reproductive and survival rates were affected by clothianidin resistance in CT-R strain compared with the CT-S strain of A. gossypii. Overall, our results demonstrate that in-depth knowledge about the trade-off at play between resistance degree and fitness cost might be useful to design resistance management strategies against A. gossypii.

Introduction

The melon aphid, Aphis gossypii Glover (Hemiptera: Aphididae), is an economically important insect pest, affecting Cucurbitaceae plants worldwide [START_REF] Hulle | Encyclop'Aphid: a website on aphids and their natural enemies[END_REF]. Aphis gossypii causes severe economic losses by direct feeding i.e., deforming the young leaves and twigs and indirectly by transmitting several plant viruses [START_REF] Hulle | Encyclop'Aphid: a website on aphids and their natural enemies[END_REF][START_REF] Ebert | Biology and ecology of Aphis gossypii Glover (Homoptera: aphididae)[END_REF]. Various control methods were used [START_REF] Ullah | RNAi-Mediated Knockdown of Chitin Synthase 1 (CHS1) Gene Causes Mortality and Decreased Longevity and Fecundity in Aphis gossypii[END_REF][START_REF] Tariq | RNA interference-mediated knockdown of voltage-gated sodium channel (MpNav) gene causes mortality in peach-potato aphid, Myzus persicae[END_REF][START_REF] Li | The outbreaks of non-target mirid bugs promote arthropod pest suppression in Bt cotton agroecosystems[END_REF], but still, synthetic chemical insecticides are considered crucial to combat aphids [START_REF] Ullah | Impact of low lethal concentrations of buprofezin on biological traits and expression profile of chitin synthase 1 gene (CHS1) in melon aphid, Aphis gossypii[END_REF][START_REF] Mohammed | Impact of imidacloprid and natural enemies on cereal aphids: Integration or ecosystem service disruption?[END_REF][START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF], despite negative effects on beneficial insects [START_REF] Desneux | The sublethal effects of pesticides on beneficial arthropods[END_REF][START_REF] Lu | Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services[END_REF][START_REF] Decourtye | Overhaul pesticide testing on bees[END_REF][START_REF] Jam | Sublethal effects of imidacloprid and pymetrozine on the functional response of the aphid parasitoid, Lysiphlebus fabarum[END_REF][START_REF] Passos | Lethal, sublethal and transgenerational effects of insecticides on Macrolophus basicornis, predator of Tuta absoluta[END_REF][START_REF] Taning | With or without foraging for food, field-realistic concentrations of sulfoxaflor are equally toxic to bumblebees (Bombus terrestris)[END_REF]. However, the indiscriminate applications of chemical insecticides cause resistance development in insect pests, including A. gossypii [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Koo | Regional susceptibilities to 12 insecticides of melon and cotton aphid, Aphis gossypii (Hemiptera: Aphididae) and a point mutation associated with imidacloprid resistance[END_REF][START_REF] Cui | Cycloxaprid: a novel cis-nitromethylene neonicotinoid insecticide to control imidacloprid-resistant cotton aphid (Aphis gossypii)[END_REF][START_REF] Ullah | Functional analysis of cytochrome P450 genes linked with acetamiprid resistance in melon aphid, Aphis gossypii[END_REF][START_REF] Ullah | Imidacloprid-induced hormesis effects on demographic traits of the melon aphid, Aphis gossypii[END_REF][START_REF] Wang | Comparison of full-length transcriptomes of different imidacloprid-resistant strains of Rhopalosiphum padi (Linne ´)[END_REF].

Neonicotinoid insecticides, including clothianidin, have been widely used against several insect pests [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF][START_REF] Ullah | Imidacloprid-induced hormesis effects on demographic traits of the melon aphid, Aphis gossypii[END_REF][START_REF] Ullah | Acetamiprid-induced hormetic effects and vitellogenin gene (Vg) expression in the melon aphid, Aphis gossypii[END_REF][START_REF] Kollmeyer | Discovery of the nitromethylene heterocycle insecticides. Nicotinoid insecticides and the nicotinic acetylcholine receptor[END_REF][START_REF] Goulson | An overview of the environmental risks posed by neonicotinoid insecticides[END_REF][START_REF] Zhang | Effects of imidacloprid and clothianidin seed treatments on wheat aphids and their natural enemies on winter wheat[END_REF]. Neonicotinoids act as an agonist of nicotinic acetylcholine receptors (nAChRs), causing nerve stimulation, paralysis, and death [START_REF] Tomizawa | Neonicotinoid insecticide toxicology: mechanisms of selective action[END_REF][START_REF] Sallam | Effectiveness of different bio-rational insecticides applied on wheat plants to control cereal aphids[END_REF][START_REF] Armstrong | The effect of planting time insecticides and liquid fertilizer on the Russian wheat aphid (Homoptera: Aphididae) and the lesion nematode (Pratylenchus thornei) on winter wheat[END_REF]. Owing to stomach and contact insecticidal activity, neonicotinoids are considered one of the most widely used insecticide group against several insect pests including aphids, whiteflies, jassids, leafminers, thrips, monarch butterfly and many species of beetles (Coleoptera: Curculionidae) [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF][START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Tomlin | The pesticide manual: A world compendium: British Crop Production Council[END_REF][START_REF] Jeschke | Thiacloprid (Bay YRC 2894)-A new member of the chloronicotinyl insecticide (CNI) family[END_REF][START_REF] Magalhaes | Baseline susceptibility of western corn rootworm (Coleoptera: Chrysomelidae) to clothianidin[END_REF][START_REF] Zhang | Efficacy of granular applications of clothianidin and nitenpyram against Aphis gossypii (Glover) and Apolygus lucorum (Meyer-Du ¨r) in cotton fields in China[END_REF][START_REF] Rahman | Evaluation of thiacloprid and clothianidin (neonicotinoids) as alternative to fenthion (organophosphate) for control of Mediterranean fruit fly (Diptera: Tephritidae) in deciduous fruit orchards[END_REF][START_REF] Zhang | Dissipation dynamics of clothianidin and its control efficacy against Bradysia odoriphaga Yang and Zhang in Chinese chive ecosystems[END_REF][START_REF] Ullah | Thiamethoxam induces transgenerational hormesis effects and alteration of genes expression in Aphis gossypii[END_REF][START_REF] Kaiser | Susceptibility of Danish pollen beetle populations against lambda-cyhalothrin and thiacloprid[END_REF][START_REF] Lin Q-C, Chen | Improved control of Frankliniella occidentalis on greenhouse pepper through the integration of Orius sauteri and neonicotinoid insecticides[END_REF]. Clothianidin is a second-generation neonicotinoid insecticide broadly used for controlling aphids [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF][START_REF] Sallam | Effectiveness of different bio-rational insecticides applied on wheat plants to control cereal aphids[END_REF][START_REF] Zhang | Efficacy of granular applications of clothianidin and nitenpyram against Aphis gossypii (Glover) and Apolygus lucorum (Meyer-Du ¨r) in cotton fields in China[END_REF]. The resistant populations of A. gossypii suffer adverse effects on life history traits in the absence of the insecticide [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF]. The LC 15 of clothianidin affects the longevity, fecundity, and demographic parameters (R 0 and GRR) of A. gossypii [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF]. Acetamiprid is also a neonicotinoid insecticide widely used against several sap-sucking insect pests, including A. gossypii. [START_REF] Ullah | Acetamiprid-induced hormetic effects and vitellogenin gene (Vg) expression in the melon aphid, Aphis gossypii[END_REF][START_REF] Singh | Bio effectiveness of combination product of acetamiprid with cypermethrin, quinalphos and chlorpyriphos against insect pests of cotton[END_REF][START_REF] Raghuraman | Bioefficacy of acetamiprid on sucking pests in cotton[END_REF][START_REF] Aslam | Comparative efficacy of different insecticides against sucking pests of cotton[END_REF][START_REF] Khattak | Efficacy of certain insecticides against some sucking insect pests of mungbean (Vigna radiata L.)[END_REF]. Both the longevity and fertility of A. gossypii were decreased when exposed to the LC 5 and LC 15 concentrations of acetamiprid [START_REF] Ullah | Acetamiprid-induced hormetic effects and vitellogenin gene (Vg) expression in the melon aphid, Aphis gossypii[END_REF].

Fitness cost affects insecticide resistance evolution and the rate of resistance increase in insects [START_REF] Abbas | Dominant fitness costs of resistance to fipronil in Musca domestica Linnaeus (Diptera: Muscidae)[END_REF][START_REF] Kliot | Fitness costs associated with insecticide resistance[END_REF]. Resistance costs energy and that may influence fitness in the absence of the insecticide stressor [START_REF] Kliot | Fitness costs associated with insecticide resistance[END_REF]. Prior studies showed evidence about the fitness costs associated with insecticide resistance in several insects including Bradysia odoriphaga Yang and Zhang (Diptera: Sciaridae), Thrips hawaiiensis Morgan (Thysanoptera: Thripidae), Plutella xylostella, Linnaeus (Lepidoptera: Plutellidae), Nilaparvata lugens Stål (Hemiptera: Delphacidae) N. lugens and Musca domestica Linnaeus (Diptera: Muscidae). Ma et al. reported fitness costs of 0.917 in the sulfoxaflor resistant population of A. gossypii compared to the susceptible strain [START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF]. The relative fitness had also been decreased up to 0.83 in the sulfoxaflor resistant strain of Myzus persicae Sulzer (Homoptera: Aphididae) [START_REF] Wang | Laboratory selection for resistance to sulfoxaflor and fitness costs in the green peach aphid Myzus persicae[END_REF]. The life-history traits of B. odoriphaga have significantly been affected in clothianidin and chlorfenapyr resistant populations compared to the susceptible strain [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. Several studies have reported the development of insecticide resistance accompanied by fitness costs in different insect pests [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Cao | Quantitative Analysis of Fitness Costs Associated with the Development of Resistance to the Bt Toxin Cry1Ac in Helicoverpa armigera[END_REF][START_REF] Ejaz | Spirotetramat resistance selected in the Phenacoccus solenopsis (Homoptera: Pseudococcidae): Cross-resistance patterns, stability, and fitness costs analysis[END_REF][START_REF] Hoffmann | Fitness costs of resistance to Cry3Bb1 maize by western corn rootworm[END_REF][START_REF] Petzold-Maxwell | Fitness costs associated with Cry1F resistance in the European corn borer[END_REF].

To our knowledge, no study to date has examined selection-induced clothianidin resistance development accompanied with fitness costs in A. gossypii. The overall goal of this work was to analyze the risk of clothianidin resistance in A. gossypii under continuous selection pressure. To examine the impact of clothianidin resistance on fitness, we used the age-stage two-sex life table approach to accurately quantify the life history traits of resistant (CT-R) and susceptible (CT-S) strains of A. gossypii. This gives an in-depth knowledge about the optimal application of clothianidin insecticides against A. gossypii.

Materials and methods

Insects and insecticide

Aphis gossypii were initially collected from melon plants at Weifang City, Shandong Province, China. The population was maintained under standard laboratory conditions (25 ± 1˚C; 75% RH; 16:8 L: D) at China Agricultural University using insecticide-free cucumber plants. Technical grade clothianidin (95% of the active ingredient) was purchased from Bayer CropScience Co. Ltd (Monheim, Germany). Triton X-100 was obtained from Sigma-Aldrich Co. Saint Louis, USA.

Toxicity of clothianidin against A. gossypii

The bioassays of clothianidin were conducted under laboratory conditions using the leaf-dipping procedure. The stock solution of clothianidin was prepared in acetone, and further dilution was set up in distilled water containing 0.05% (v/v) Triton X-100. Cucumber leaf discs were dipped in the required diluted concentrations of clothianidin or in 0.05% (v/v) Triton X-100 water as a control for 15 s. The treated discs were allowed to dry on the disposable transparent plastic gloves and then placed the adaxial side of leaf discs on 2% (w/v) agar bed (2 ml) in a 12-well cell culture plate. Twenty adult melon aphids were placed on each leaf disc, and Chinese art paper (Xuan rice paper) was used to cover the plate to prevent the aphid's escape. There were three leaf disks for each concentration, and the entire experiment was repeated three times for a total of 180 aphids tested at each concentration. The mortality was recorded at 72 h after treatment. The LC 50 values of clothianidin were calculated by probit analysis using POLO Plus 2.0 statistical software.

Establishing the resistant colony

The resistant strain of clothianidin (CT-R) was established from an originally collected susceptible population of A. gossypii through continuous selection pressure for 24 generations. The acute toxicity of clothianidin was recorded for each generation. Based on the results of the bioassays of the parent aphids, the clothianidin concentrations were gradually increased throughout the selection experiment. The mortality rate was maintained at 60-80%. The resistance ratio (RR) was determined at each generation by dividing the LC 50 of resistant strain to the LC 50 of the susceptible strain of A. gossypii. The susceptible strain (CT-S) was maintained on cucumber plants without any pre-or post-exposure of clothianidin. Both strains were kept under standard laboratory conditions (25 ± 1 ˚C; 75% RH; 16:8 L: D) in the Department of Entomology, China Agricultural University, Beijing, China.

Fitness comparisons

Fitness of the susceptible and resistant strains of A. gossypii was compared using age-stage, two-sex life table approach. About 500 apterous adults were inoculated to insecticide-free cucumber seedlings. After 24 h, ninety newly born nymphs of A. gossypii were collected from both susceptible and resistant populations. Both strains were transferred to insecticide-free cucumber seedlings and were maintained separately under laboratory conditions. Each individual aphid growing on one insecticide-free cucumber seedling was considered as a single replicate [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF][START_REF] Ullah | Imidacloprid-induced hormesis effects on demographic traits of the melon aphid, Aphis gossypii[END_REF]. Nymphs from both populations were observed individually and we recorded development duration, mortality, longevity, and fecundity. The life table data of susceptible (CT-S) and resistant (CT-R) strains of A. gossypii were subjected to the TWOSEX-MSChart computer program [START_REF] Chi | TWOSEX-MS Chart: A computer program for the age-stage, two-sex life table analysis[END_REF] to analyze the population parameters including age-stage specific survival rates (s xj ), age-specific survival rate (l x ), age-specific fecundity (m x ), age-specific maternity (l x m x ), age-stage specific life expectancy (e xj ) and age-stage reproductive value (v xj ) following age-stage two-sex life table procedure [START_REF] Chi | Age-Stage, two-sex life table: an introduction to theory, data analysis, and application[END_REF]. The s xj shows the probability that a newly born nymph will survive to age x and stage j. The l x represents a simplified form of the survival history and the probability that a newly-born nymph will survive to age x. The m x indicates the age-specific fecundity, while the l x m x shows age-specific maturity. The v xj depicts the devotion to future offspring for A. gossypii individuals of at age x and stage j. The e xj describes the expected duration of time an individual of age x at stage j that will survive after the age x. The newly born nymphs produced by females during the reproductive period were counted and removed daily. Fresh cucumber seedlings were replaced after 5 days without any insecticide exposure throughout the experiment. The aphids were individually transferred to new seedlings using a soft brush.

Statistical analysis

The age-stage two-sex life table procedure was applied to analyze the developmental duration, adult longevity, and fecundity for all individual aphids using the TWOSEX-MSChart computer program [START_REF] Chi | TWOSEX-MS Chart: A computer program for the age-stage, two-sex life table analysis[END_REF][START_REF] Chi | Age-Stage, two-sex life table: an introduction to theory, data analysis, and application[END_REF][START_REF] Chi | Two new methods for the study of insect population ecology[END_REF][START_REF] Chi | Life-table analysis incorporating both sexes and variable development rates among individuals[END_REF]. The population parameters including the intrinsic rate of increase (r), finite rate of increase (λ), net reproductive rate (R 0 ), mean generation time (T), gross reproduction rate (GRR), adult pre-oviposition period (APOP), total pre-oviposition period (TPOP), oviposition days (Od), age-stage specific survival rates (s xj ), age-specific survival rate (l x ), age-specific fecundity (m x ), age-specific maternity (l x m x ), age-stage specific life expectancy (e xj ) and age-stage reproductive value (v xj ) were investigated following Chi and Liu [START_REF] Chi | Two new methods for the study of insect population ecology[END_REF] and Chi [START_REF] Chi | Life-table analysis incorporating both sexes and variable development rates among individuals[END_REF] using TWOSEX-MSChart computer program [START_REF] Chi | TWOSEX-MS Chart: A computer program for the age-stage, two-sex life table analysis[END_REF]. The means and standard errors of the population parameters between CT-S and CT-R strains were analyzed using paired bootstrap tests via 100,000 bootstrap replicates [START_REF] Chi | Age-Stage, two-sex life table: an introduction to theory, data analysis, and application[END_REF][START_REF] Huang | Linking demography and consumption of Henosepilachna vigintioctopunctata (Coleoptera: Coccinellidae) fed on Solanum photeinocarpum (Solanales: Solanaceae): with a new method to project the uncertainty of population growth and consumption[END_REF][START_REF] Huang | Life tables of Bactrocera cucurbitae (Diptera: Tephritidae): with an invalidation of the jackknife technique[END_REF]. All figures were constructed using Sig-maPlot 12.0 (Systat Software Inc., San Jose, CA, USA).

Results

Clothianidin resistance development

The clothianidin resistant strain (CT-R) was established from the susceptible strain (CT-S) through continuous exposure with clothianidin for 24 generations under controlled conditions (Table 1).

The LC 50 value of the CT-S was 0.38 mg L -1 . In the first 10 generations (F 2 -F 10 ) of the CT-R population, the LC 50 values were slowly increased from 0.52 mg L -1 to 2.23 mg L -1 . However, these values were steeply increased in the following generations (F 12 -F 24 ) with LC 50 values 2.79, 3.52, 4.47, 5.56, 6.78, 7.72, and 8.76 mg L -1 , respectively. After the selection for 24 generations, the resistance ratio was increased (23.17-fold) compared to the CT-S strain (Table 1).

Impact of clothianidin resistance on the life history traits of A. gossypii

The life-history traits, including developmental time, longevity, fecundity, and oviposition days between clothianidin resistant (CT-R) and susceptible strains (CT-S) of A. gossypii are presented in Table 2. The mean developmental durations of 1 st instar, 3 rd instar, and 4 th instar nymph of CT-R strain was significantly shorter than that of the CT-S aphids (P <0.001). The pre-adult period, adult duration, and total pre-oviposition period (TPOP) were significantly shorter in CT-R strain compared to the CT-S strain (Table 2). No significant differences were observed for the adult pre-oviposition period (APOP) between both strains of A. gossypii. The oviposition days, total longevity and fecundity were significantly lower in CT-R strain of A. gossypii (P <0.05).

The demographic traits (r, λ, R 0 , T, and GRR) of CT-R and CT-S strains of A. gossypii were evaluated by a paired bootstrap technique based on the life table (Table 3). When compared to CT-S strain, the r and λ of CT-R strain were significantly increased (P <0.001). While the R 0 , T, and GRR in CT-R strain were markedly decreased as compared to the CT-S strain of A. gossypii (Table 3). In the absence of insecticide exposure, the overall fitness of clothianidin-resistant strain (CT-R) of A. gossypii was 0.847 as compared to the susceptible strain (CT-S).

The s xj shows the probability of neonate nymph that will survive to age x and stage j. The overlaps among different stages occurred due to the stage differentiations between CT-R and CT-S individuals (Fig 1). The s xj curves show apparent differences, with a lower survival rate of nymphal and adult female stages in CT-R strain compared to the CT-S strain. The adult female survival rate of CT-R strain started to decline on 10 th day, while decline occurred on 12th day in CT-S strain (Fig 1).

The l x , m x and l x m x differences among CT-R and CT-S strains are presented in Fig 2 . The l x curves show a lower survival rate of CT-R strain at the age of 12-32 days compared to the CT-S strain of A. gossypii (Fig 2). The maximal survival duration of CT-R strain was 32 days, c Difference between strains were compared with paired bootstrap test. If the CI includes 0, there is no difference at 5% level.

Table 2. Mean (± SE) life history parameters of the susceptible (CT-S) and resistant (CT-R) strains of Aphis gossypii.

Stages

� Significant differences between resistant strain (CT-R) and susceptible strain (CT-S) at P = 0.05 level, paired bootstrap test using TWOSEX MS chart program.

https://doi.org/10.1371/journal.pone.0238707.t002

which was lower than that of the CT-S strain (34 days). The m x and l x m x values in CT-R strain was decreased after 18 th day, which shows lower fecundity as compared to the CT-S strain (Fig 2). In the first 12 days, values of m x and l x m x for CT-R strain were higher as compared to CT-S strain; however, after that, this trend has been reversed, and CT-R strain displayed lower fecundity as compared to the CT-S strain (

Discussion

Chemical applications are still crucial for the control of A. gossypii in China [START_REF] Ullah | Impact of low lethal concentrations of buprofezin on biological traits and expression profile of chitin synthase 1 gene (CHS1) in melon aphid, Aphis gossypii[END_REF][START_REF] Chen | Sublethal and transgenerational effects of sulfoxaflor on the biological traits of the cotton aphid, Aphis gossypii Glover (Hemiptera: Aphididae)[END_REF][START_REF] Yuan | Lethal, sublethal and transgenerational effects of the novel chiral neonicotinoid pesticide cycloxaprid on demographic and behavioral traits of Aphis gossypii (Hemiptera: Aphididae)[END_REF][START_REF] Nahiyoon | Biocidal radiuses of cycloxaprid, imidacloprid and lambda-cyhalothrin droplets controlling against cotton aphid (Aphis gossypii) using an unmanned aerial vehicle[END_REF].

Clothianidin belongs to second-generation neonicotinoid insecticide that acts as an agonist of the nicotinic acetylcholine receptors (nAChRs) [START_REF] Armstrong | The effect of planting time insecticides and liquid fertilizer on the Russian wheat aphid (Homoptera: Aphididae) and the lesion nematode (Pratylenchus thornei) on winter wheat[END_REF]. Due to excellent insecticidal activity, clothianidin has been broadly used against many insect pests including hemipterans and many species of beetles [START_REF] Ullah | Clothianidin-induced sublethal effects and expression changes of vitellogenin and ecdysone receptors genes in the melon aphid, Aphis gossypii[END_REF][START_REF] Tomlin | The pesticide manual: A world compendium: British Crop Production Council[END_REF][START_REF] Jeschke | Thiacloprid (Bay YRC 2894)-A new member of the chloronicotinyl insecticide (CNI) family[END_REF][START_REF] Magalhaes | Baseline susceptibility of western corn rootworm (Coleoptera: Chrysomelidae) to clothianidin[END_REF][START_REF] Zhang | Efficacy of granular applications of clothianidin and nitenpyram against Aphis gossypii (Glover) and Apolygus lucorum (Meyer-Du ¨r) in cotton fields in China[END_REF][START_REF] Rahman | Evaluation of thiacloprid and clothianidin (neonicotinoids) as alternative to fenthion (organophosphate) for control of Mediterranean fruit fly (Diptera: Tephritidae) in deciduous fruit orchards[END_REF][START_REF] Zhang | Dissipation dynamics of clothianidin and its control efficacy against Bradysia odoriphaga Yang and Zhang in Chinese chive ecosystems[END_REF]. However, the insecticidal actions have been dramatically affected by the development of insecticide resistance [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. There have been numerous studies to investigate the development of insecticide resistance against various pests such as Chive maggot, Colorado potato beetle, small brown plant hopper, western flower thrips, peach aphid and tobacco aphid [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Zhao | Insecticide resistance in field and laboratory strains of western flower thrips (Thysanoptera: Thripidae)[END_REF][START_REF] Grafius | Resistance to imidacloprid in Colorado potato beetles from Michigan[END_REF][START_REF] Elbert | Resistance of Bemisia tabaci (Homoptera: Aleyrodidae) to insecticides in southern Spain with special reference to neonicotinoids[END_REF][START_REF] Cahill | Baseline determination and detection of resistance to imidacloprid in Bemisia tabaci (Homoptera: Aleyrodidae)[END_REF]. The resistance development accompanied by fitness costs significantly affect the evolution of insecticide resistance [START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. To our knowledge, no previous research has investigated the development of clothianidin resistance and associated fitness costs in the melon aphid, A. gossypii. Therefore, an in-depth study about the resistance development and fitness costs of a resistant population of A. gossypii could be crucial for an effective control measure against this insect pest. We showed that A. gossypii developed 23.17-fold clothianidin resistance following 24 generations of exposure to increasing concentrations of clothianidin. Recent studies by Ullah et al. and Gul et al. concluded 43.32-and 76-fold resistance in B. odoriphaga following 10 consecutive generations selection to chlorfenapyr and clothianidin, respectively [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. Beet armyworm, Spodoptera exigua Hu ¨bner (Lepidoptera: Noctuidae) showed 69.76-and 113.29-fold resistance against deltamethrin and gossypol, respectively following 10 generations selections [START_REF] Hafeez | Gossypol-induced fitness gain and increased resistance to deltamethrin in beet armyworm, Spodoptera exigua (Hu ¨bner)[END_REF]. There have been numerous studies to investigate selection-induced resistance in many d R f = R 0 of the resistant strain (CT-R)/ R 0 of the susceptible strain (CT-S).

� Significant differences between resistant strain (CT-R) and susceptible strain (CT-S) at P = 0.05 level, paired bootstrap test using TWOSEX MS chart program.

https://doi.org/10.1371/journal.pone.0238707.t003

insect and insecticide combinations [START_REF] Liu | Widespread resistance of the aphid Myzus persicae to pirimicarb across China, and insights on ace2 mutation frequency in this species[END_REF][START_REF] Silva | Field-evolved resistance to chlorantraniliprole in the tomato pinworm Tuta absoluta: inheritance, cross-resistance profle, and metabolism[END_REF][START_REF] Liu | Fitness costs of laboratory-selected imidacloprid resistance in the brown planthopper, Nilaparvata lugens Stål[END_REF][START_REF] Zewen | Selection for imidacloprid resistance in Nilaparvata lugens: cross-resistance patterns and possible mechanisms[END_REF][START_REF] Guedes | Insecticide resistance in the tomato pinworm Tuta absoluta: patterns, spread, mechanisms, management and outlook[END_REF]. The resistance ratio was lower in CT-R strain of A. gossypii, as compared to other insects and insecticides. The difference might be due to differences in the field collected material, their prior exposure to insecticides, and the length of time the colony was maintained insecticide-free in the laboratory. The resistance ratio might be higher if the population exposed to clothianidin for more generations, which is a future prospect. Also, different insect species show different responses to insecticides. Fitness costs associated with resistance have been broadly studied in several insect species including B. odoriphaga, T. hawaiiensis, P. xylostella, N. lugens and M. domestica [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Abbas | Dominant fitness costs of resistance to fipronil in Musca domestica Linnaeus (Diptera: Muscidae)[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Fu | Resistance development, stability, cross-resistance potential, biological fitness and biochemical mechanisms of spinetoram resistance in Thrips hawaiiensis (Thysanoptera: Thripidae)[END_REF][START_REF] Steinbach | Fitness costs and life table parameters of highly insecticideresistant strains of Plutella xylostella (L.)(Lepidoptera: Plutellidae) at different temperatures[END_REF][START_REF] Zhang | Fitness cost of nitenpyram resistance in the brown planthopper Nilaparvata lugens[END_REF]. In the current study, two strains with similar genetic backgrounds have been used to accurately assess the resistance-linked fitness costs [START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. The results have shown the shorter developmental duration of 1 st instar, 3 rd instar, and 4 th instar nymphs of CT-R strain as compared to the CT-S strain of A. gossypii. The pre-adult period of CT-R was also shorter than that of the CT-S aphids. This shows that the clothianidin resistance development could facilitate the nymphal growth in A. gossypii. As has been previously reported in the literature, suggesting the decreased developmental durations of nymphal stages and pre-adult period in sulfoxaflorresistant A. gossypii [START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF]. Several studies have reported similar phenomena in M. persicae and A. gossypii resistant populations [START_REF] Wang | Laboratory selection for resistance to sulfoxaflor and fitness costs in the green peach aphid Myzus persicae[END_REF][START_REF] Wang | Dominant fitness costs of abamectin resistance in Plutella xylostella[END_REF]. The developmental duration was also decreased in deltamethrin and gossypol resistant strains of S. exigua compared to susceptible strain [START_REF] Hafeez | Gossypol-induced fitness gain and increased resistance to deltamethrin in beet armyworm, Spodoptera exigua (Hu ¨bner)[END_REF]. In contrast, others have shown increased developmental durations in the resistant insect pests compared to the susceptible strain [START_REF] Zhang | Fitness cost of nitenpyram resistance in the brown planthopper Nilaparvata lugens[END_REF][START_REF] Cui | Resistance selection of indoxacarb in Helicoverpa armigera (Hu ¨bner)(Lepidoptera: Noctuidae): cross-resistance, biochemical mechanisms and associated fitness costs[END_REF]. For example, the developmental period of B. odoriphaga significantly enhanced in clothianidin resistant strain [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF].

In current study, the adult longevity, TPOP, oviposition period, total longevity, and fecundity were decreased significantly in CT-R strain. From the results, it is clear that the clothianidin resistance developed at the cost of the reduced fecundity. Overall these findings are in accordance with findings reported by Ullah et al. and Gul et al. that development of insecticide resistance affects the life-history traits, including fecundity and longevity of the resistant strain https://doi.org/10.1371/journal.pone.0238707.g002 [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. The longevity and fecundity were decreased significantly in deltamethrin and gossypol resistant strains of S. exigua [START_REF] Hafeez | Gossypol-induced fitness gain and increased resistance to deltamethrin in beet armyworm, Spodoptera exigua (Hu ¨bner)[END_REF]. The shorter longevity (9.55%) and fecundity (15%) were also observed in the resistant strain of M. persicae [START_REF] Wang | Laboratory selection for resistance to sulfoxaflor and fitness costs in the green peach aphid Myzus persicae[END_REF]. Resistance-induced fitness costs have been reported in several other insect pests [START_REF] Steinbach | Fitness costs and life table parameters of highly insecticideresistant strains of Plutella xylostella (L.)(Lepidoptera: Plutellidae) at different temperatures[END_REF][START_REF] Cao | Tebufenozide resistance selected in Plutella xylostella and its cross-resistance and fitness cost[END_REF][START_REF] Abbas | Fitness cost, cross resistance and realized heritability of resistance to imidacloprid in Spodoptera litura (Lepidoptera: Noctuidae)[END_REF][START_REF] Carrière | Effects of gossypol on fitness costs associated with resistance to Bt cotton in pink bollworm[END_REF][START_REF] Sayyed | Fitness costs limit the development of resistance to indoxacarb and deltamethrin in Heliothis virescens (Lepidoptera: Noctuidae)[END_REF][START_REF] Wang | Reduced fitness associated with spinosad resistance in Helicoverpa armigera[END_REF].

The demographic traits can explain the growth potential of insect pest populations [START_REF] Khan | Effect of livestock manures on the fitness of house fly, Musca domestica L.(Diptera: Muscidae)[END_REF][START_REF] Saeed | Effect of different host plants on the fitness of diamond-back moth, Plutella xylostella (Lepidoptera: Plutellidae)[END_REF]. The r and λ were significantly increased in CT-R compared to the CT-S. However, the R 0 and T were decreased in clothianidin resistant strain of A. gossypii. The findings are directly in line with previous findings of Ma et al. showing that the r and λ were increased, while R 0 and T were decreased in sulfoxaflor-resistant A. gossypii [START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF]. The R 0 was lowered in the laboratory selected resistant strains of M. persicae and B. odoriphaga [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Wang | Laboratory selection for resistance to sulfoxaflor and fitness costs in the green peach aphid Myzus persicae[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. The demographic traits, including r, λ, R 0 , and T were also affected by clothianidin and gossypol resistance in S. exigua [START_REF] Hafeez | Gossypol-induced fitness gain and increased resistance to deltamethrin in beet armyworm, Spodoptera exigua (Hu ¨bner)[END_REF].

The s xj , l x , m x , l x m x , e xj and v xj were significantly decreased in clothianidin resistant strain of A. gossypii. Our findings are consistent with Ullah et al. and Gul et al. showing similar results in clothianidin and chlorfenapyr resistant strain of B. odoriphaga [START_REF] Gul | Resistance against clothianidin and associated fitness costs in the chive maggot, Bradysia odoriphaga[END_REF][START_REF] Ullah | Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga[END_REF]. Many prior reports have showed similar effects on several insect and insecticide combinations including sulfoxaflor-resistant A. gossypii [START_REF] Ma | Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover[END_REF], imidacloprid-resistant S. litura [START_REF] Abbas | Fitness cost, cross resistance and realized heritability of resistance to imidacloprid in Spodoptera litura (Lepidoptera: Noctuidae)[END_REF], deltamethrin and indoxacarb-resistant Heliothis virescens Fabricius (Lepidoptera:Noctuidae) [START_REF] Sayyed | Fitness costs limit the development of resistance to indoxacarb and deltamethrin in Heliothis virescens (Lepidoptera: Noctuidae)[END_REF] and spinosadresistant P. xylostella [START_REF] Sayyed | Genetic, biochemical, and physiological characterization of spinosad resistance in Plutella xylostella (Lepidoptera: Plutellidae)[END_REF]. From the results, it is clear that A. gossypii has the potential to develop resistance against widely used clothianidin insecticide. Our study also provides a comprehensive understanding of the fitness costs in CT-R as compared to CT-S.

Conclusion

Overall, our results show clothianidin resistance development (23.17-fold) in A. gossypii under continuous selection over 24 generations. Moreover, there are fitness costs in the resistant population, owing to the selection of resistance to clothianidin in A. gossypii. These findings will be useful for understanding clothianidin resistance and associated fitness costs in A. gossypii. However, future research on the underlying molecular mechanisms might extend the explanations of clothianidin resistance in A. gossypii.

Fig 2 )

 2 . The e xj curves in CT-R strain show the shorter survival expectancy of the developmental as well as adult stage compared to the CT-S strain of A. gossypii (Fig 3). The v xj shows the devotion of individuals of age x and stage j towards future offspring (Fig 4). The pattern of v xj was recorded lower for the CT-R strain as compared to the CT-S of A. gossypii (Fig 4).

Fig 1 .

 1 Fig 1. Age-stage specific survival rate (s xj ) in susceptible (CT-S) and resistant (CT-R) strains of A. gossypii. https://doi.org/10.1371/journal.pone.0238707.g001

Fig 2 .

 2 Fig 2. Age-specific survival rate (l x ), age-specific fecundity (m x ) and age-specific maternity (l x m x ) in susceptible (CT-S) and resistant (CT-R) strains of A. gossypii.

Fig 3 .

 3 Fig 3. Age-stage specific life expectancy (e xj ) in susceptible (CT-S) and resistant (CT-R) strains of A. gossypii. https://doi.org/10.1371/journal.pone.0238707.g003

Fig 4 .

 4 Fig 4. Age-stage reproductive value (v xj ) in susceptible (CT-S) and resistant (CT-R) strains of A. gossypii. https://doi.org/10.1371/journal.pone.0238707.g004

Table 1 . The resistance level of Aphis gossypii to clothianidin. Generations LC 50 (95%CI) a mg L -1 Slope ± SE b χ 2 P-value RR c

 1

	F0	0.38 (0.28-0.49)	1.95±0.24	16.50	0.223	-
	F2	0.52 (0.41-0.66)	1.62 ± 0.21	3.02	0.998	1.38
	F4	0.86 (0.67-1.16)	1.55 ± 0.22	2.40	0.999	2.27
	F6	1.29 (1.00-1.86)	1.61 ± 0.23	2.49	0.999	3.43
	F8	1.55 (1.27-1.92)	2.02 ± 0.24	3.51	0.995	4.09
	F10	2.23 (1.73-3.15)	1.55 ± 0.22	2.92	0.998	5.91
	F12	2.79 (2.33-3.38)	2.25 ± 0.28	4.12	0.990	7.39
	F14	3.52 (2.88-4.50)	1.94 ± 0.25	2.62	0.999	9.31
	F16	4.47 (3.83-5.17)	2.94 ± 0.45	7.62	0.868	11.83
	F18	5.56 (4.72-6.79)	2.38 ± 0.34	3.22	0.997	14.70
	F20	6.78 (5.76-8.54)	2.63 ± 0.42	2.46	0.999	17.93
	F22	7.72 (6.42-9.26)	2.15 ± 0.23	2.56	0.999	20.43
	F24	8.76 (6.95-11.06)	1.65 ± 0.21	3.25	0.997	23.17
	Number of larvae exposed in bioassay, including control = 360; df = 13.

a 95% confidence intervals. b Standard error.

c RR, resistance ratio, determined as (LC 50 of resistant strain/LC 50 of susceptible strain). https://doi.org/10.1371/journal.pone.0238707.t001

Susceptible strain (CT-S) Resistant strain (CT-R) 95% CI c P-value n a Mean ± SE b n a Mean ± SE b

	First instar nymph (days)	90	1.99 ± 0.07	90	1.61 ± 0.08	(0.170, 0.585) �	<0.001
	Second instar nymph (days)	89	1.63 ± 0.08	89	1.51 ± 0.07	-0.091, 0.339)	0.258
	Third instar nymph (days)	87	1.72 ± 0.09	85	1.33 ± 0.07	(0.173, 0.616) �	0.001
	Fourth instar nymph (days)	86	1.92 ± 0.06	83	1.54 ± 0.09	(0.170, 0.583) �	<0.001
	Pre-adult (days)	86	7.28 ± 0.10	83	6.01 ± 0.08	(1.014, 1.521) �	<0.001
	Adult (days)	86	19.16 ± 0.48	83	16.17 ± 0.57	(1.528, 4.461) �	<0.001
	APOP (days)	86	0.23 ± 0.05	83	0.25 ± 0.06	(-0.178, 0.137)	0.798
	TPOP (days)	86	7.51 ± 0.11	83	6.27 ± 0.10	(0.950, 1.544) �	<0.001
	Oviposition days	86	19.41 ± 1.16	83	14.41 ± 0.51	(2.459, 7.536) �	<0.001
	Total longevity (days)	90	25.46 ± 0.68	90	20.77 ± 0.75	(2.692, 6.686) �	<0.001
	Fecundity (offspring/individual)	86	41.38 ± 0.96	83	36.35 ± 1.15	(2.081, 7.988) �	0.001

a Number of subjects. b Standard errors (SE) were estimated using the bootstrap technique with 100,000 re-samplings.

Table 3 . Mean (± SE) demographic parameters of the susceptible (CT-S) and resistant (CT-R) strains of Aphis gossypii.

 3 r: intrinsic rate of increase, λ: finite rate of increase, R 0 : net reproductive rate, T: mean generation time, GRR: gross reproductive rate. Standard errors (SE) were estimated using the bootstrap technique with 100,000 re-samplings. c Difference between strains were compared with paired bootstrap test. If the CI includes 0, there is no difference at 5% level.

	Population parameters a	(Mean ± SE b )

a b

PLOS ONE | https://doi.org/10.1371/journal.pone.0238707 September 14, 2020

Acknowledgments

We thank Prof. Dr. Hsin Chi (Department of Entomology, National Chung Hsing University, Taiwan), for their statistical assistance with the two-sex life table theory. We are also grateful to two anonymous reviewers for valuable and profound comments.

This work was financially supported by the National Key Research and Development Program of China (2016YFD0200500) and the National Natural Science Foundation of China (31272077).

All relevant data are within the manuscript and its Supporting Information files.

Ethics approval

No ethics approval was required for this research.

Supporting information

S1 Table. Life table of susceptible strain (CT-S) of A. gossypii. (XLSX)

S2 Table. Life table of clothianidin-resistant strain (CT-R) of A. gossypii. (XLSX)

Author Contributions

Conceptualization: Farman Ullah, Nicolas Desneux, Dunlun Song.

Data curation: Farman Ullah.

Formal analysis: Farman Ullah, Hina Gul.