Development Of Control Strategies Research Articles

Strategi Inovatif Pemberantasan Hama Jagung Dengan Sistem Fuzzy Dan Iot Untuk Meningkatkan Produktivitas (Studi Kasus Pemberantasan Hama Jagung Di Ponorogo)

Corn pest control is a significant challenge for corn farmers in Indonesia. Pests can cause substantial losses for farmers, especially if left unattended. Therefore, innovative strategies are needed to enhance productivity and reduce farmer losses. This study explores the potential of Internet of Things (IoT) technology in innovative corn pest control strategies. The study aims to investigate how IoT can assist corn farmers in monitoring and controlling pests, thereby enhancing productivity and reducing losses.The study employs a case study approach, focusing on the application of IoT technology in corn pest control. Data collection involves field observations, interviews with farmers, and analysis of existing literature. The findings indicate that IoT technology can significantly improve the accuracy and speed of pest monitoring, enabling farmers to make more informed decisions about pest control.The study concludes that the integration of IoT technology into corn pest control strategies can lead to increased productivity and reduced losses for farmers. The findings of this study contribute to the development of innovative and effective pest control strategies, ultimately benefiting the corn farming industry in Indonesia.

Intra-event variations of organic micropollutants in highway runoff and a presedimentation-biofilter treatment facility

The study assessed the quality of highway runoff and a stormwater treatment system, focusing on intra-event variations (IEVs: variations within a runoff/effluent event) of the concentration of organic micropollutants (OMPs) including bisphenol-A, alkylphenols, polycyclic aromatic hydrocarbons (PAHs), and petroleum hydrocarbons (PHCs). IEVs of OMPs varied considerably with no particular recurring pattern in highway runoff and presedimentation effluent, displaying sporadic strong first flushes. IEVs are significantly associated with rainfall intensity variations, especially for particle-bound substances such as PAHs and PHCs. However, phenolic substances showed distinct IEV patterns compared to total suspended solids, PAHs, and PHCs, likely due to their higher solubility and mobility. Downstream sand filter (SF) and vegetated biofilter (BFC) mitigated IEVs, leading to more uniform discharge during outflow events. Although BFC’s IEVs were indiscernible due to low effluent concentrations, SF’s IEVs often peaked at the beginning of events (within the first 100 of ⁓600 m3), exceeding the lowest predicted non-effect concentrations for five PAHs, bisphenol-A, and octylphenol. This study highlights the advantage of IEV analysis over conventional event mean concentration analysis for identifying critical effluent stages, crucial for developing control strategies to protect sensitive water recipients or for reuse applications.

Improved Sliding Mode Control for Tracking Global Maximum Power of Triple Series Parallel Ladder Photovoltaic Array under Uneven Shadowing

Abstract This paper presents an innovative way for enhancing the performance of photovoltaic arrays under uneven shadowing conditions. The study focuses on a triple series parallel ladder configuration to exploit the benefits of increased power generation while addressing the challenges associated with uneven shadowing. The proposed methodology focuses on the implementation of improved sliding mode Control technique for the global maximum power point tracking efficiently. Sliding mode control is known for its robustness in the presence of uncertainties and disturbances, making it suitable for dynamic and complex systems such as photovoltaic arrays. This work employs a comprehensive simulation framework to comment the performance of the suggested improved sliding mode control strategy at uneven shadowing scenarios. Comparative analysis has been done to show the effectiveness of the suggested method than the traditional control strategies. The results demonstrate a remarkable enhancement in the tracking accuracy of the global maximum power point, leading to enhanced energy harvesting capabilities under challenging environmental conditions. Furthermore, the proposed approach exhibits robustness and adaptability in mitigating the effect of shading on the photovoltaic array, thereby increasing overall system efficiency. This research contributes valuable insights into the development of advanced control strategies for photovoltaic arrays, particularly in the context of triple series parallel ladder configurations operating under uneven shadowing conditions. Under short narrow shading condition, the improved sliding mode control method tracking the maximum power more compared to perturb & observe is 20.68%, incremental conductance is 68.78%, fuzzy incremental conductance is 19.8%, and constant velocity sliding mode control is 1.25%. The improved sliding mode control method has 60% less chattering than constant velocity sliding mode control under shading conditions.

A Critical Review in Infectious Disease and Mathematical Modelling

Infectious diseases control and prevention relies on a thorough understanding of the factors determining transmission. The transmission dynamics of infectious diseases is susceptible to changes governed by several factors, whose recognition is critical for the rational development of strategies for prevention and control, as well as for developing health policies. This article summarizes the fundamental principles of infectious disease transmission and compartmental models. Basic principles of infectious disease diagnosis, reasons for formulating the infectious diseases and historical facts on epidemics are also reviewed.

Setosphaeria turcica, the Maize Leaf Blast Pathogen: Current Status and Infection Mechanisms

The fungal pathogen Setosphaeria turcica causes northern corn leaf blight (NCLB) in maize, sorghum, and related grasses. NCLB is a serious fungal foliar disease of cultivated maize that causes devastating yield losses. S. turcica infects maize leaves by means of a specialized cell, the appressorium, but the regulatory mechanisms that underlie appressorium-mediated infection remain largely unknown. Many regulatory pathways and a large number of genes have been described in S. turcica, and many of these genes have been cloned. Characterization of such disease-related genes is important for understanding the biological mechanisms of interaction between pathogen and host and can guide the development of strategies for disease control. There is a significant level of concern regarding the possible dissemination of the S. turcica pathogen to regions where NCLB is not presently prevalent. This scenario is of considerable concern and necessitates immediate research intervention. The present review brings together information on the epidemiology and infection mechanisms of S. turcica.

Setosphaeria turcica, the Maize Leaf Blast Pathogen: Current Status and Infection Mechanisms

The fungal pathogen Setosphaeria turcica causes northern corn leaf blight (NCLB) in maize, sorghum, and related grasses. NCLB is a serious fungal foliar disease of cultivated maize that causes devastating yield losses. S. turcica infects maize leaves by means of a specialized cell, the appressorium, but the regulatory mechanisms that underlie appressorium-mediated infection remain largely unknown. Many regulatory pathways and a large number of genes have been described in S. turcica, and many of these genes have been cloned. Characterization of such disease-related genes is important for understanding the biological mechanisms of interaction between pathogen and host and can guide the development of strategies for disease control. There is a significant level of concern regarding the possible dissemination of the S. turcica pathogen to regions where NCLB is not presently prevalent. This scenario is of considerable concern and necessitates immediate research intervention. The present review brings together information on the epidemiology and infection mechanisms of S. turcica.

A Critical Review in Infectious Disease and Mathematical Modelling

Infectious diseases control and prevention relies on a thorough understanding of the factors determining transmission. The transmission dynamics of infectious diseases is susceptible to changes governed by several factors, whose recognition is critical for the rational development of strategies for prevention and control, as well as for developing health policies. This article summarizes the fundamental principles of infectious disease transmission and compartmental models. Basic principles of infectious disease diagnosis, reasons for formulating the infectious diseases and historical facts on epidemics are also reviewed.

Effects of Hypoxia on the Antibacterial Activity of Epidermal Mucus from Chilean Meagre (Cilus gilberti).

Comprehending the immune defense mechanisms of new aquaculture species, such as the Chilean meagre (Cilus gilberti), is essential for sustaining large-scale production. Two bioassays were conducted to assess the impact of acute and intermittent hypoxia on the antibacterial activity of juvenile Chilean meagre epidermal mucus against the potential pathogens Vibrio anguillarum and Vibrio ordalii. Lysozyme and peroxidase activities were also measured. In general, fish exposed to hypoxia showed a 9-30% reduction in mucus antibacterial activity at the end of hypoxic periods and after stimulation with lipopolysaccharide. However, following water reoxygenation, the activity of non-stimulated fish was comparable to that of fish in normoxic conditions, inhibiting bacterial growth by 35-52%. In the case of fish exposed to chronic hypoxia, the response against V. anguillarum increased by an additional 19.8% after 6 days of control inoculation. Lysozyme exhibited a similar pattern, while no modulation of peroxidase activity was detected post-hypoxia. These results highlight the resilience of C. gilberti to dissolved oxygen fluctuations and contribute to understanding the potential of mucus in maintaining the health of cultured fish and the development of future control strategies.

Effects of Hypoxia on the Antibacterial Activity of Epidermal Mucus from Chilean Meagre (Cilus gilberti)

Comprehending the immune defense mechanisms of new aquaculture species, such as the Chilean meagre (Cilus gilberti), is essential for sustaining large-scale production. Two bioassays were conducted to assess the impact of acute and intermittent hypoxia on the antibacterial activity of juvenile Chilean meagre epidermal mucus against the potential pathogens Vibrio anguillarum and Vibrio ordalii. Lysozyme and peroxidase activities were also measured. In general, fish exposed to hypoxia showed a 9–30% reduction in mucus antibacterial activity at the end of hypoxic periods and after stimulation with lipopolysaccharide. However, following water reoxygenation, the activity of non-stimulated fish was comparable to that of fish in normoxic conditions, inhibiting bacterial growth by 35–52%. In the case of fish exposed to chronic hypoxia, the response against V. anguillarum increased by an additional 19.8% after 6 days of control inoculation. Lysozyme exhibited a similar pattern, while no modulation of peroxidase activity was detected post-hypoxia. These results highlight the resilience of C. gilberti to dissolved oxygen fluctuations and contribute to understanding the potential of mucus in maintaining the health of cultured fish and the development of future control strategies.

Quantifying the effect of driving restrictions on fine particulate matter concentrations with WRF-Chem model: A case study in Kaifeng, China

One of the significant sources of urban air particulate matter (PM2.5 and PM10) has been found to be vehicle exhaust emission. Vehicle restriction measures have been taken to alleviate particulate matter pollution in the world. Here, we quantified the impact of vehicle restriction measures on the PM2.5 concentrations using the Weather Research and Forecasting model with Chemistry (WRF-Chem) in Kaifeng, China. This study set up three different scenarios to simulate the spatiotemporal changes of PM2.5 concentrations in four seasons in 2020. They were no emission reduction (S0), the restriction of two tailing license plate numbers (S1), and the restriction of odd–even tailing numbers of license plates (S2), respectively. In general, the odd–even license plate restriction policy has proven to be more effective than restricting two specific license plate numbers. In scenario S1, the range of PM2.5 concentrations reduction in Kaifeng City was between 0.2% and 1.28%. In scenario S2, the range of PM2.5 concentrations reduction was between 0.98% and 14.42%. The results showed that the effectiveness of vehicle restriction measures varies from season to season, and the effect in summer and winter is better than that in spring and autumn. We suggest that it may be difficult to reduce the PM2.5 concentrations to a great extent if simply reducing the traffic exhaust emissions through motor vehicle restrictions, on the contrary, would lead to the increase of O3 concentration, bringing some other effects. This study can serve as a foundation for the rational development of motor vehicle control strategies and help advance the ongoing improvement of air quality.

Performance-prescribed optimal neural control for hypersonic vehicles considering disturbances: An adaptive dynamic programming approach

The precise trajectory tracking control of hypersonic vehicles plays a crucial role in secure flight. To address this challenge, this article develops an optimal neural control framework with prescribed performance constraints for height-velocity control tasks with disturbances. To this end, the control-oriented error model is firstly established and transformed into an affine nonlinear form to provide a foundation for the design of controller. Then, in order to balance performance constraints and convergence speed, a performance-constraint function is introduced to convert the constrained tracking error into an unconstrained one. After that, an improved sliding mode compensation observer is proposed by combining low-fidelity approximation data with high-fidelity compensation model for eliminating disturbances. The developed control scheme is organized by an adaptive dynamic programming (ADP) algorithm based on the actor-critic policy structure to achieve the near-optimal control strategy. Meanwhile, the designed neural network (NN) weight updating laws are provided to obtain the solution of Hamiltonian-Jacobi-Bellman (HJB) equation rapidly without high calculation consumption. Finally, the stability conditions of the closed-loop system are performed and ensure the entire framework is semi-global uniformly ultimately bounded (SGUUB), two simulation cases verify the effectiveness and superiority of the proposed approach.

GPCR–MAPK signaling pathways underpin fitness trade-offs in whitefly

Trade-offs between evolutionary gain and loss are prevalent in nature, yet their genetic basis is not well resolved. The evolution of insect resistance to insecticide is often associated with strong fitness costs; however, how the fitness trade-offs operates remains poorly understood. Here, we show that the mitogen-activated protein kinase (MAPK) pathway and its upstream and downstream actors underlie the fitness trade-offs associated with insecticide resistance in the whitefly Bemisia tabaci. Specifically, we find a key cytochrome P450 gene CYP6CM1, that confers neonicotinoids resistance to in B. tabaci, is regulated by the MAPKs p38 and ERK through their activation of the transcription factor cAMP-response element binding protein. However, phosphorylation of p38 and ERK also leads to the activation of the transcription repressor Cap "n" collar isoform C (CncC) that negatively regulates exuperantia (Ex), vasa (Va), and benign gonial cell neoplasm (Bg), key genes involved in oogenesis, leading to abnormal ovary growth and a reduction in female fecundity. We further demonstrate that the transmembrane G protein-coupled receptor (GPCR) neuropeptide FF receptor 2 (NPFF2) triggers the p38 and ERK pathways via phosphorylation. Additionally, a positive feedback loop between p38 and NPFF2 leads to the continuous activation of the MAPK pathways, thereby constitutively promoting neonicotinoids resistance but with a significant reproductive cost. Collectively, these findings provide fundamental insights into the role of cis-trans regulatory networks incurred by GPCR-MAPK signaling pathways in evolutionary trade-offs and applied knowledge that can inform the development of strategies for the sustainable pest control.

Adaptive command filter control for switched time‐delay systems with dead‐zone based on an exact disturbance observer

AbstractThe focus of this article lies in the adaptive command filter tracking control problems for a class of switched time‐delay systems with an asymmetric dead‐zone and external disturbance under arbitrary switching. First, the external disturbance is estimated via an exact disturbance observer. Then, an echo state network (ESN) was adopted to approximate unknown function in system without tuning the weights between a reservoir and an input layer. Second, the time‐varying input of the dead‐zone model is considered as uncertain physical quantity of the system based on the dead‐zone slope boundary information. Third, an adaptive command filtered controller is employed to conquer the matter of “explosion of complexity” encountered in the traditional backstepping method. The filtering errors present in a dynamic surface control (DSC) approach are effectively eliminated by means of error compensation signals. A prescribed performance control (PPC) is resorted to confine the system output to meet prescribed steady‐state and transient tracking performances. The time delay terms are compensated by a Lyapunov–Krasovskii functional. The semi‐globally ultimately uniformly boundedness of all states in the closed‐loop system is ensured by the Liapunov's stability criterion. Finally, the validity of the developed control scheme is further verified through a simulation example.

Quantitative evaluation of the role of aging and non-aging factors for predicting threats from major chronic diseases and developing control strategies

Various indicators exist to assess the threat of chronic diseases. This paper presents new ones to evaluate the role of aging and non-aging factors for predicting threats from major chronic diseases. Age at zero mortality (AM0) and age at average mortality (AMa) can be calculated by regressing age and mortality (the intercept indicates AM0, the slope indicates the observed slope and r indicates random non-aging factors). A regression equation can be created using AMa at the age of 72 and mortality at the age of 82; thus, the expected slope can be obtained for the aging factor without considering non-aging factors. It is possible to distinguish between aging and non-aging factors using the observed and expected slopes, which should be multiplied by r to produce an index of aging (IA). The lower the AM0, AMa or IA of a disease is, the greater the threat it poses to a population. The AM0 and IA were calculated using data from China (2004 and 2019) for various diseases [cancer, heart disease (HD), cerebrovascular disease (CVD), and chronic obstructive pulmonary disease (COPD)]. We found the severity of threat was highest for cancer, CVD, other chronic diseases, HD and COPD in descending order in 2019. The results suggest that changes in threats may be related to socioeconomic development. Cancer was found to be the greatest threat to younger age groups, with IA<0.5, suggesting that non-aging risk factors may play an important role in cancers. Conversely, aging may play an important role in other chronic diseases, including HD, CVD, and COPD. Compared to 2004, the AM0 of cancer showed the greatest change. In conclusions, the different indicators explain different aspects of the problem and it would be beneficial to conduct in-depth research on the theoretical basis for the association of threats of disease with socioeconomic development in order to develop prevention and control strategies.

Seroprevalence of hepatitis c virus in Blood Donors in Kabul in 2023

Introduction: Understanding the prevalence and distribution of the hepatitis C virus (HCV) is crucial for the development of effective prevention and control strategies, given its significant impact on global public health. The objective of this study was to examine the seroprevalence of HCV among blood donors in Kabul and assess the possible gender-based variations in infection rates. Materials and Methods: This study analyzed blood donor records from the central blood bank in Kabul, including both male and female donors, from January to December 2023. HCV screening was performed using ELISA testing, and the data were analyzed using SPSS to determine HCV prevalence and investigate gender-based differences in infection rates. Results: In this study, 59 out of 15,080 individuals (0.39%) tested positive for HCV infection. Among the HCV-positive cases, 81.5% were male and 18.6% were female. The majority of infected males (64.4%) were in the 30-39 age range. A significant proportion of HCV-positive patients had no formal education (69.5%) and were unemployed (61.0%). Additionally, the majority of HCV-positive patients reported a low monthly income (66.1%). Discussion: The study found a low prevalence of HCV infection (0.39%) among the participants, with the highest rates observed in the 30-39 age group. Socioeconomic factors, such as lack of education, unemployment, and low income, were associated with HCV infection. Targeted interventions are needed to address gender disparities, age-specific risks, and socioeconomic determinants to improve prevention and control strategies for HCV in Kabul's blood donor population and enhance overall population health.

Interpretable GHG emission prediction for papermaking wastewater treatment process with deep learning

The content of pollutants in papermaking wastewater is highly varied due to heterogeneous raw materials, product types, and biochemical process conditions, resulting in significant uncertainty of GHG emissions mitigation in the treatment process. This study established a modified Benchmark Simulation Model no.1-based mechanism model to reveal the characteristics of GHG emissions and support GHG reduction in the papermaking wastewater treatment process. Due to its heavy dependence on the Benchmark Simulation Model No. 1 (BSM1) and plentiful parameters, it was instead further proposed Deep Neural Networks (DNN) models and Long Short-Term Memory (LSTM) models to estimate GHG emissions with high accuracy (R2 > 0.94) greatly reduced computational complexity (8920.6 times). The global sensitivity analysis upon DNN revealed key factors for GHG emission with strong explainability, supporting the development of GHG reduction control strategies for the papermaking wastewater treatment process.

Adaptive fuzzy fixed-time bipartite consensus control for stochastic nonlinear multi-agent systems with performance constraints

This paper investigates the fixed-time bipartite consensus control problem of stochastic nonlinear multi-agent systems (MASs) with performance constraints. A constraint scaling function is proposed to model the performance constraints with user-predefined steady-state accuracy and settling time without relying on the initial condition. Technically, the local synchronization error of each follower is mapped to a new error variable using the constraint scaling function and an error transformation function before being used to design the controller. To achieve fixed-time convergence of the local tracking error, a barrier function transforms the scaled synchronization error to a new variable to guarantee the prescribed performance. Then, an adaptive fuzzy fixed-time bipartite consensus controller is developed. The fuzzy logic system handles the uncertainties in the designing procedures, and one adaptive parameter needs to be estimated online. It is shown that the closed-loop system has practical fixed-time stability in probability, and the antagonistic network’s consensus error evolves within user-predefined performance constraints. The simulation results evaluate the effectiveness of the developed control scheme.

Reinforcement Learning-Based Control Sequence Optimization for Advanced Reactors

The last decade has seen the development and application of data-driven methods taking off in nuclear engineering research, aiming to improve the safety and reliability of nuclear power. This work focuses on developing a reinforcement learning-based control sequence optimization framework for advanced nuclear systems, which not only aims to enhance flexible operations, promoting the economics of advanced nuclear technology, but also prioritizing safety during normal operation. At its core, the framework allows the sequence of operational actions to be learned and optimized by an agent to facilitate smooth transitions between the modes of operations (i.e., load-following), while ensuring that all safety significant system parameters remain within their respective limits. To generate dynamic system responses, facilitate control strategy development, and demonstrate the effectiveness of the framework, a simulation environment of a pebble-bed high-temperature gas-cooled reactor was utilized. The soft actor-critic algorithm was adopted to train a reinforcement learning agent, which can generate control sequences to maneuver plant power output in the range between 100% and 50% of the nameplate power through sufficient training. It was shown in the performance validation that the agent successfully generated control actions that maintained electrical output within a tight tolerance of 0.5% from the demand while satisfying all safety constraints. During the mode transition, the agent can maintain the reactor outlet temperature within ±1.5 °C and steam pressure within 0.1 MPa of their setpoints, respectively, by dynamically adjusting control rod positions, control valve openings, and pump speeds. The results demonstrate the effectiveness of the optimization framework and the feasibility of reinforcement learning in designing control strategies for advanced reactor systems.

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data

Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The MAE of the algorithm is recorded as 20.1 s, with an RMSE of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety.