Conventional Control Research Articles

Validation and test of a novel multi-input converter in extreme conditions using PSOPIC in hybrid power generation environment

The need for renewable energy resources increases due to the day-by-day increase in load demand. Still, there is a need for new technology to operate the existing power system optimally. Distributed generation (DGENs) are helpful in meeting the demand of power due to its lower cost compared to the construction of the complete power system. But this DGEN is constructed using renewable resources, which are intermittent in nature. So, hybrid power generation, which uses multiple sources, is used to satisfy the power need. In this paper, validation, and test of a new multi-input single ended primary inductor converter (MI-SEPIC) is proposed. The performance of the MI-SEPIC converter is tested by connecting photovoltaic (PV), wind, and fuel cells. The proposed system is connected to the grid, and the power transients are analyzed. The DQ control of grid synchronization is discussed in this paper. The conventional PI controller is replaced with a hybrid particle swarm optimization tuned proportional integral control (PSOPIC), and the results are compared. Verification is done using MATLAB software. Validation and test with different test cases to prove the sturdiness of the complete system is explained.

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

Least mean square based adaptive control of active power filter

The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.

Primary side control technique for capacitive power transfer system without any wireless feedback

The output voltage of capacitive power transfer (CPT) system will change if the load resistance is varied. This paper presents a method to regulate the output voltage using a controller that is located on the primary side, known as the primary side control technique. It does not require any additional components and wireless feedback, which lowers the cost and complexity of CPT system compared to the conventional control technique. Instead of directly measuring the output voltage on secondary side, it is estimated through the measured capacitor voltage on primary side. Modified sine wave control of the full-bridge inverter is adopted to regulate the output voltage. The proposed control technique is validated by the simulation via PSIM software using the practical parameters of capacitive coupler presented in the literature. Simulation results of the output voltage control against the step change in desired output voltage and load resistance indicate the performance of proposed control technique.

Simulation and modeling for controlling stepper motor with tuned PID by GWO: comparative study

The current work aims to simulate the operation of the electric motor in one of the most important industrial applications, which is printers, by adopting stepper motors (SM). The performance of the motor is also improved by adopting traditional control systems and adjusting them using the gray wolf optimization (GWO) advanced algorithm. It works to adjust the parameters of a conventional controller. Simulation to reach an appropriate design with high performance, which is obtained by adopting the integral time absolute error (ITAE) function to get rid of the error for transient cases. Transfer function was adopted to represent the engine and two methods of control were used, traditional and advanced optimization. Results demonstrated the possibility of improving performance by adopting both methods with a clear superiority of advanced optimization. Response of SM without controller for close loop shows the values of each rising time equal 130.440 ms, overshoot equal 0.505%, and undershoot equal 1.077%. Response of SM for close loop with proportional-integral-derivative controllers (PIDC) shows the parameters, performance, and robustness of PIDC also the values of overshoot=9.16%, settling time=0.406, and rise time=0.0628 s. Results were developed by using GWO-PID over the previous cases by reducing values of overshoot to zero, rise time, and settling time to 0.00145 and 0.0027 respectively.

Design of Interactive Online Teaching System for Big Data Computer Information System Based on Mobile Sensor Network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Plateform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

A novel PQ improvement in multi-parallel feeder distribution system using multi-convertible UPQC device

In the current situation, the use of non-linear power electronic devices in automated industries, arc-furnaces, and adjustable speed drives. The nature of large-sized non-linear loads causes harmonic pollution, voltage interruptions, and voltage sag/swell issues, which are the main issues in modern multi-parallel feeder distribution network. Several mitigation techniques among the above, the multi-convertible universal power-quality conditioner has reliable performance, robust operation and also mitigation of both current-voltage allied power quality (PQ) issues accordingly. The multi-convertible universal power-quality conditioner (Mcon-UPQC) device mitigates power-quality issues by extracting deviated supply voltage and distorted load current by using feasible control algorithm. But, the computational delay, complex mathematical transformation and response delay are the major problem identified in conventional control algorithms. In this work, a novel generalized vector reference control algorithm has been proposed for extraction of fundamental reference voltage-current signals. The performance and effectiveness of proposed control algorithm of Mcon-UPQC device is validated by using MATLAB-Simulink computing tool, simulated outcomes are illustrated with valid comparisons.

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

The transition from traditional to renewable energy sources is a critical issue in current energy-generation systems, which aims to address climate change and the increased demand for energy. This shift, however, imposes additional burdens on control systems to maintain power system stability and quality within predefined limits. Addressing these challenges, this paper proposes an innovative Modified Hybrid Fractional-Order (MHFO) automatic voltage regulator (AVR) equipped with a fractional-order tilt integral and proportional derivative with a filter plus a second-order derivative with a filter FOTI-PDND2N2 controller. This advanced controller combines the benefits of a (FOTI) controller, known for enhancing dynamic performance and steady-state response, with a (PDND2N2) controller to improve system robustness and adaptability. The proposed MHFO controller stands out with its nine tunable parameters, providing more extensive control options than the conventional three-parameter PID controller and the five-parameter FOPID controller. Furthermore, a recent optimization approach using a growth optimizer (GO) has been formulated and applied to optimally adjust the MHFO controller’s parameters simultaneously. The performance of the proposed AVR based on the MHFO-GO controller is scrutinized by contrasting it with various established and developed optimization algorithms. The comparative study shows that the AVR based on the MHFO-GO controller surpasses other AVR controllers from the stability, robustness, and dynamic response speed points of view.

Artificial Neural Network Control Applied to a Photovoltaic-Battery Microgrid System

This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.

Definitive single-stage surgery for treating diabetic foot osteomyelitis: a protocolized pathway including antibiotic bone graft substitute use.

Diabetic foot ulcers (DFUs) are a challenging complication of diabetes mellitus, often leading to poor clinical outcomes and significant socioeconomic burdens. We evaluated the effectiveness of a definitive single-stage protocolized surgical management pathway, including the use of local antibiotic bone graft substitute, for the treatment of infected DFUs with associated osteomyelitis. A retrospective cohort study was conducted. Medical records were extracted (from January 2017 to December 2020) to establish a database consisting of patients who underwent surgical intervention for the treatment of an infected DFU with osteomyelitis. Patients were divided into conventional (control) and protocolized (intervention) surgical groups depending on the treatment received. Clinical outcomes were assessed over a 12-month follow-up period. A total of 136 consecutive patients were included (conventional = 33, protocolized = 103). The protocolized group demonstrated a statistically significant reduction in the mean number of operations performed per patient (1.2 vs. 3.5) (P < 0.001) and a shorter accumulative hospital length of stay (12.6 vs. 25.1 days) (P < 0.001) compared to the conventional group. Major amputation rates were significantly lower in the protocolized group (2% vs. 18%) (P < 0.001). Within 12 months of surgical intervention, the protocolized group exhibited an ulcer healing rate of 89%, with a low rate of recurrence (3%). The protocolized surgical pathway, including local antibiotic bone graft substitute use, demonstrated superior outcomes compared to conventional management for the treatment of infected DFUs with osteomyelitis. Further research is needed to evaluate the cost-effectiveness and generalizability of this approach.

Terminal Synergetic Control for Plate Heat Exchanger

Terminal synergetic control (TSC) is proposed as a control strategy for the temperature management of a plate heat exchanger. The controller is designed by incorporating a selected macro variable with a time-varying sliding surface. The primary objective is to maintain precise control over the outlet temperature of the cold water. To assess the convergence characteristics of the newly proposed TSC approach, the simulation results achieved using TSC featuring a time-varying macro variable are compared to those obtained from the conventional synergetic control (SC) method. With an appropriate macro variable, the simulation results indicate a notable improvement in the convergence rate provided by our designed TSC method, compared to the conventional one. The desirable property of control input, the chattering-free condition, achieved by both TSC and SC approaches emphasizes the advantage of the synergetic control-based techniques over the conventional sliding mode controller. In conclusion, synergetic control-based techniques offer superior potential solutions for nonlinear feedback control problems.

Temperature control research for laser-induction hybrid strengthening process

ABSTRACT A comprehensive control methodology has been developed to address challenges such as temperature fluctuations, inconsistent depth penetration, and compositional integrity variations during laser-induction hybrid strengthening performed with consistent power parameters. This approach synergistically integrates a fuzzy controller and a decoupling compensator. In the initial part of this study, a temperature field model was utilized to investigate the effects of different process factors on temperature profiles. The analysis of step response data facilitated the development of mathematical models that delineate the correlation between the input factors and output responses. Subsequently, a decoupling compensation technique was used to reduce the coupling effects between temperature and its rate of change. A fuzzy controller was then developed to reduce the system's reliance on mathematical models, hence improving the accuracy of the control strategy. The results indicate that the application of fuzzy PI control and the implementation of decoupling compensation, the control strategy successfully improves the performance of the controller by minimizing parameter interactions. This strategy also incorporates real-time adjustment of PI parameters. Compared to conventional PI control, this method offers improved response speed and reduced overshoot. The laser-induction hybrid strengthening technique ensures superior temperature control, maintaining steady-state temperature precision within a 1% tolerance.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

A novel quadrotor carrying payload concept via PID with Feedforward terms

PurposeThis paper introduces control strategy to enhance the performance of a novel quadrotor unmanned aerial vehicle designed for medical payload delivery. The aim is to achieve precise control and stability when carrying and releasing payloads, which alter the quadrotor’s mass and inertia characteristics.Design/methodology/approachThe equations of motion specific to the payload-carrying quadrotor are derived. A feedforward-proportional-integral-derivative (FF-PID) control strategy is then proposed to address the dynamic changes during payload release. The PID components use propeller speed/orientation information for stability. FF terms based on derivatives of desired position/orientation variables enable adaptation to real-time mass fluctuations.FindingsExtensive simulations, encompassing various fault scenarios, substantiate the effectiveness of the FF-PID approach. Notably, our findings demonstrate superior performance in maintaining altitude precision and stability during critical phases such as takeoff, payload release and landing. Graphical representations of thrust and mass dynamics distinctly illustrate the payload release event. In contrast to the linear quadratic regulator (LQR) and conventional PID control, which encountered difficulties during the payload release process, our approach proves its robustness and reliability.Research limitations/implicationsThis study, primarily based on simulations, demands validation through real-world testing in diverse conditions. Uncertainties in dynamic parameters, external factors and the applicability of the proposed approach to other quadrotor configurations require further investigation. Additionally, this research focuses on controlled payload release, leaving unexplored the challenges posed by unforeseen scenarios or disturbances. Hence, adaptability and fault tolerance necessitate further exploration. While our work presents a promising approach, practical implementation, adaptability and resilience to unexpected events are vital considerations for future research in the field of autonomous aerial medical deliveries.Practical implicationsThe proposed control strategy promises enhanced efficiency, reliability and adaptability for autonomous aerial medical deliveries in critical scenarios.Social implicationsThe innovative control strategy introduced in this study holds the potential to significantly impact society by enhancing the reliability and adaptability of autonomous aerial medical deliveries. This could lead to faster and more efficient delivery of life-saving supplies to remote or disaster-affected areas, ultimately saving lives and reducing suffering. Moreover, the technology’s adaptability may have broader applications in fields like disaster relief, search and rescue missions, and industrial cargo transport. However, its successful integration into society will require careful regulation, privacy safeguards and ethical considerations to ensure responsible and safe deployment while addressing potential concerns related to noise pollution and privacy intrusion.Originality/valueWhile PID control of quadrotors is extensively studied, payload release dynamics have been overlooked. This research studies integration of FF control to enable PID adaptation for a novel payload delivery application.

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

On the impact of Industrial Internet of Things (IIoT) - mining sector perspectives

ABSTRACT The application of the Internet of Things (IoT) in industrial systems and other new technological advancements led to the development of the Industrial Internet of Things (IIoT). IIoT can help overcome the shortcomings of the conventional monitoring and control system while enabling enterprises to create a unified monitoring system to automate operations, provide a safe working environment, enforce compliance effectively, and regulate environmental issues. Given the advantages that IoT brings to the forefront, it makes sense that so many mining companies have raised their investment following the implementation of IoT-enabled solutions in their organisations. To increase safety, productivity, and environmental sustainability in mining operations, this article examines the current state of information technology in the mineral industry with an emphasis on the implications and challenges brought on by the technological diversity of various systems and devices used in those activities. The study contributes valuable insights into the integration of IoT technologies in the mining industry, highlighting its potential to improve safety, efficiency, and sustainability. The paper paves the way for future research and development efforts aimed at overcoming the challenges of adopting IIoT in mining operations by identifying gaps and proposing a comprehensive IoT architecture. The innovations of the study are encapsulated in its approach to detailing the application of IoT technologies in enhancing mining operations and the establishment of an overall IIoT architecture suitable for the general operations in the mining industry.

The effect of temperature and exposure time on redroot pigweed (Amaranthus retroflexus) and yellow foxtail (Setaria pumila) seed mortality in the natural soil seed bank

Abstract Heat disinfection of soil can be used to reduce the content of the soil seed bank. However, species differ in the lethal temperature needed for seed destruction and mortality. Laboratory research was conducted on the seeds of two weed species, redroot pigweed (Amaranthus retroflexus L.) and yellow foxtail [Setaria pumila (Poir.) Roem. et Schult]. The soil samples were collected at the experimental station Šašinovečki Lug, Zagreb (45.850289°N; 16.180465°W), Croatia and exposed to linearly increasing constant temperatures of 40, 50, 60, 80, 100 and 120 C and exposure times of 30, 60 and 90 min in a laboratory oven. Weed seeds were then extracted from the soil using the sieve separation method and survival measured by germinating seeds on the filter paper. Germination counts were converted into percentages of mortality compared to untreated seeds. The results show that both temperature and exposure time significantly affected seed mortality of both weed species. A. retroflexus shows a greater susceptibility to high temperatures than S. pumila. Fitted three-parameter sigmoid model was used to define the relationship between temperature and exposure time needed for 50% (LT50) and 90% (LT90) seed mortality. The estimated LT50 values for A. retroflexus are 58.89 – 46.08 C over the 30 to 90 min exposure times; the estimated LT90 113.36 to 65.72 C for the same durations. The estimated LT50 values for S. pumila over the 30 to 90 min exposure times ranged from 91.33 to 75.15 C; the estimated LT90 ranged from 98.79 to 90.32 C over the same durations. The research results contribute to the knowledge about the thermal sensitivity of seeds. Estimating efficacy of soil heating treatments is essential when comparing the environmental, economic and social costs of alternatives to conventional weed control methods.

Identification, synthesis, and field evaluation of components of the female-produced sex pheromone of Helopeltis cinchonae (Hemiptera: Miridae), an emerging pest of tea.

Helopeltis cinchonae (Hemiptera: Miridae) is a major pest of tea plantations in Asia. Conventional control of pests with pesticides is unsustainable. Therefore, safe and eco-friendly alternatives, such as pheromones, are required to manage the pest. In gas chromatography-electroantennographic detection (GC-EAD) analysis of whole-body extracts of virgin female H. cinchonae, two compounds elicited electroantennogram (EAG) responses from male antennae. These were identified as hexyl (R)-3-acetoxybutyrate and (R)-1-acetoxy-5-butyroxyhexane using gas chromatography-mass spectrometry (GC-MS) analysis compared to synthetic compounds. This is the first study to report 1-acetoxy-5-butyroxyhexane as an insect pheromone component. The synthetic compounds elicited dose-dependent EAG responses from the antennae of male H. cinchonae. In two field trapping experiments, the individual compounds were highly attractive to male H. cinchonae when dispensed from polyethylene vials. However, higher catches were obtained with blends of the two compounds in a 1:10 ratio. The blend of racemic compounds was as attractive as the blend of (R)-enantiomers. We reported that 1-acetoxy-5-butyroxyhexane and hexyl 3-acetoxybutyrate are components of the female-produced sex pheromone of H. cinchonae, but further work is required on the blend and loading of pheromone and on trap design to provide an optimized system for monitoring and control of this pest. The results may also facilitate the identification of the pheromones of other Helopeltis species, which are major pests in many crops. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.