Control Strategy Research Articles

Multiple quadrants displacement tracking control of independent metering electro-hydraulic system

Aiming at the oscillation caused by the hydraulic cylinder of independent metering system (IMS) running between multiple working quadrants, a novel multiple quadrants switching displacement tracking control strategy is proposed in this paper. In this control strategy, both the cylinder chamber pressure under resistance and overrunning conditions are controlled by the inlet valve, and the outlet valve is utilized to control the displacement of cylinder. This method allows the hydraulic cylinder to always run in energy-efficient. When the working quadrant changes, the valve’s working modes are unchanged, which avoids oscillation caused by changes of valves working modes in principle. And a reduced-order active disturbance rejective displacement controller and an active disturbance rejective pressure controller are designed to get high-precision tracking of the displacement and pressure. Numerous experimental results show that the proposed method is reasonable and effective, hydraulic cylinder operates smoothly between multiple quadrants, the pressure and displacement controllers have good anti-interference ability and robustness, and the energy consumption can be reduced by more than 23% compared to the traditional constant pressure valve control system.

Invasive strategies for rhythm control of atrial fibrillation: a narrative review

Invasive strategies for rhythm control of atrial fibrillation: a narrative review

Emerging public health strategies in malaria control: innovations and implications

Malaria remains a significant global health challenge, particularly in regions with limited resources and tropical climates. Despite extensive efforts, the disease continues to cause significant morbidity and mortality, with approximately 229 million cases and 409,000 deaths reported in 2020. However, recent years have seen promising advancements in public health strategies aimed at malaria control and elimination. Technological advancements have played a crucial role in improving malaria control efforts. Genomic surveillance techniques enable the monitoring of malaria parasite populations, aiding in the detection of drug resistance and informing targeted interventions. Additionally, innovative diagnostic technologies, such as rapid diagnostic tests (RDTs) and molecular assays, have enhanced the speed and accuracy of malaria diagnosis, facilitated prompt treatment and reduced transmission. These tools are instrumental in achieving the World Health Organization’s (WHO) goals of reducing malaria cases and deaths by at least 90% by 2030. Novel vector control methods offer innovative approaches to reduce malaria transmission. Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) remain foundational strategies, with advancements including the development of next-generation insecticides and long-lasting insecticidal nets (LLINs). Furthermore, genetic modification of mosquitoes, such as gene drive technology, holds promise for reducing mosquito populations and interrupting malaria transmission. These vector control innovations complement other strategies, contributing to comprehensive malaria control efforts aimed at achieving sustainable disease reduction and eventual elimination.

Spatio-temporal patterns of dengue in Bangladesh during 2019 to 2023: Implications for targeted control strategies

Spatio-temporal patterns of dengue in Bangladesh during 2019 to 2023: Implications for targeted control strategies

Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs

Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs

Personalized assist-as-needed dressing assistance robot without human modeling using Rowat-Selverston CPG controller

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the ‘Assist-As-Needed’ (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology.

Sentiment Analysis of Cigarette Use Based on Opinions from X Using Naive Bayes and SVM

The research employs Naive Bayes and Support Vector Machine (SVM) classification techniques to analyze attitudes toward cigarette consumption based on Twitter user opinions. Twitter, being one of the most popular social media platforms, serves as an excellent source for gauging public sentiment on various issues, including cigarette smoking, referred to here as "X." The diverse array of opinions poses a challenge for accurate sentiment classification. This study evaluates the effectiveness of the Naive Bayes and SVM algorithms in categorizing sentiment as positive, negative, or neutral. Data is collected through web scraping, and preprocessing steps such as text cleaning, tokenization, and stemming are implemented. The performance of the classification is assessed using metrics like accuracy, precision, recall, and F1-score. The results indicate that SVM outperforms Naive Bayes in sentiment analysis related to cigarette use. These findings provide new insights into public opinion and aim to assist policymakers in developing effective tobacco control strategies.

Data integrity cyber-attack mitigation using linear quadratic regulator based load frequency control in hybrid power system

Abstract Hybrid power systems are the contemporary solution to meet the stiff climate change targets and also hold future for implementing distributed generation and microgrids. These systems have renewable sources as their key elements which in turn have high level of intermittency due to weather conditions and other reasons. At the same time, these hybrid power systems are also vulnerable to cyber threats owing to the intense adoption of various IoT Technologies (Internet of Things). Load frequency control (LFC) is a formidable task for the system operators under such scenario and robust control strategies are required to achieve the load frequency within tolerable limits. In this work Linear quadratic controller (LQR) has been proposed to address the challenges of hybrid power systems. Two area interconnected power systems are considered in this work with each one having different configuration with respect to source of power generation. Solar, wind, geo-thermal, electric vehicle charging infrastructure and pumped storage power plant are integrated together with conventional thermal power plant for showcasing the LQR based controller under data integrity cyber-attack scenario. The frequency response of the hybrid power system is demonstrated through MATLAB simulations.

Predictive Voltage Control in Multi-Modular Matrix Converters under Load Variation and Fault Scenario

This paper presents a model predictive control (MPC) strategy to regulate output voltages in a multi-modular matrix converter topology for isolated loads. The converter system harnesses power from a six-phase permanent magnet synchronous generator (PMSG) to deliver sinusoidal voltages to a three-phase load, with LC filters positioned at the output of each MC module within the multi-modular scheme. The proposed MPC approach ensures that the output voltages remain within acceptable ranges of magnitude, phase, and frequency, even under load variations and system faults. This control strategy is particularly suitable for uninterruptible power supply systems, microgrids or other applications where voltage regulation is critical. Experimental studies validate the effectiveness of the control strategy under various load conditions, reference voltage changes, and simulated system fault scenarios. The results highlight the robustness and reliability of the proposed voltage control using the multi-modular matrix converter.

A Coordinated Control Strategy for Efficiency Improvement of Multistack Fuel Cell Systems in Electric–Hydrogen Hybrid Energy Storage System

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system

In order to extract the maximum photovoltaic (PV) power under stochastic weather conditions, a maximum power point tracking (MPPT) technique is required. PV modules behave nonlinearly in unpredictable weather conditions, and the effectiveness of many control strategies under such conditions especially conventional ones—decreases significantly. It is essential to maximize the use of PV power in the system; thus, MPPT algorithms have been developed to ensure that grid integrated PV systems work best at the maximum power point regardless of weather conditions. This research presents a novel HLO-ANN (Horned Lizard optimized artificial neural network) MPPT technique for grid-integrated solar photovoltaic systems which is the improved version of the ANN-MPPT, developed using a novel HLO technique, which effectively optimizes weights of the neural network. The irradiance and temperature datasets from the NASA website have been utilized to train the neural network. The proposed methods have been evaluated by comparing their simulation results to other ANN optimized MPPT techniques: The results showed that the proposed method outperforms the other methods in terms of robustness, DC bus voltage regulation, tracking speed, convergence to the minimum error value, and tracking efficiency in maximizing the harvested power from the PV systems. Furthermore, the EN50530 MPPT efficiency test was carried out during both fast and slow-varying irradiance levels to examine the effectiveness of proposed algorithms.

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

Why becoming a positive deviant for malaria prevention and control: a sequential explanatory mixed methods study in Bugesera district, Rwanda

BackgroundMalaria continues to be a significant global health challenge, particularly in sub-Saharan African regions. Effective prevention and control strategies are crucial in mitigating its impact. Therefore, assessing the use of malaria preventive measures, treatment-seeking behaviours, and understanding the motivating factors behind positive behaviours/practices and barriers to using malaria preventive and control measures is essential for designing successful intervention programs.MethodsUsing a sequential explanatory mixed methods design, a descriptive cross-sectional study was conducted among 382 heads of households in the Mareba sector, Bugesera district, Rwanda. A qualitative study followed with 30 in-depth interviews among the top performers and other community members to explore the motivations and barriers to performing positive behaviours. Descriptive statistics for quantitative data and thematic analysis for qualitative data were used.ResultsThis study revealed that among those who own insecticide-treated nets, 234(89.3%) reported that they slept under the bed net the night preceding the survey; 256(67%) had fever cases in the last 24 months preceding the survey; and 214(87%) reported seeking care within 24 h. While almost all 243(98.8%) of participants who had fever case reported that they have taken all medicines as prescribed, however, a large number 263(68.8%) and 148(38.7%) still think that there are people in the community who do not take all malaria medications as prescribed and there are people who share malaria medications in the community, respectively. 82(65.1%) of those who never had a fever case believe that they have been using malaria preventive measures correctly and consistently. This study found that knowledge about malaria, family support, and community mobilization are the top motivating factors to practice positive behaviours while, lack of bet nets, poverty, and lack of time were reported as main barriers.ConclusionInterventions that target key motivating factors for adopting positive behaviours in malaria prevention and control should be prioritized. This, in turn, will reduce the disease burden on affected populations. Efforts to overcome barriers in malaria prevention and control should also be participatory. Community involvement should be at the centre of these interventions.

Robust Control Scheme for Optimal Power Sharing and Selective Harmonic Compensation in Islanded Microgrids

In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid’s control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%.

Noticing health warning labels and intentions to quit smokeless tobacco products: results from two Global Adult Tobacco Survey rounds, India

Introduction: Health warning labels (HWLs) are one of the key strategies for tobacco control. The current study assessed the individual characteristics affecting the noticeability of HWLs and the thought of quitting smokeless tobacco (SLT) on noticing HWLs. We further estimated the temporal changes in noticeability and thought of quitting after noticing HWLs over time in India. Methods: Data from two rounds (2009–2010 and 2016–2017) of GATS (Global Adult Tobacco Survey) from India were analysed to understand the pattern of SLT usage and estimate the relative changes in the noticeability of HWLs and the thought of quitting SLT among those who noticed HWLs. Sampling weights were used during the analysis. Multivariate binary logistic regression was used to explore the determinants for the noticeability of HWLs and encourage the intention to quit SLT after noticing the HWLs. Results: About 25.9% and 20.6% of participants consumed SLT daily or non-daily in two rounds of GATS. Overall, there was a 14% relative increase in noticing the HWLs and a 20% increase in the thought of quitting in GATS-2 compared with GATS-1. The adjusted odds ratio of observing HWLs among SLT users was 1.51 times (95% confidence interval (CI), 1.44–1.58) in round 2 compared with round 1, while the odds ratio concerning the thought of quitting SLT after noticing HWLs was 1.95 times (95% CI, 1.84–2.06) in round 2 compared with round 1. Conclusions: We observe a consistent increase in the noticeability of HWLs and intentions to quit in SLT users, with significant variations across socio-demographic characteristics calling for concerted efforts. SLT being the major form of tobacco usage in India and Asian countries, HWLs alone cannot bring down the consumption rates, and it needs to be supported by strict implementation of SLT legislation for long-lasting impact.

The feeding behavior of Diaphorina citri monitored by using an electrical penetration graph (DC-EPG) on citrus plants treated with Bacillus cereus and Bacillus velezensis

Diaphorina citri, an important pest and insect vector that can transmit the pathogenic bacteria Candidatus Liberibacter asiaticus, causing Huanglongbing disease, is one of many challenges in citrus agriculture. Integrated pest management by utilizing microorganisms is a wise and efficient alternative without damaging the environment. Utilization of Plant Growth Promoting Rhizobacteria (PGPR), such as Bacillus cereus and B. velezensis, is a potential strategy for the biological control of plant diseases or insect vectors. By inducing systemic resistance in plants, PGPR can enhance plant defense against diseases and insect pests while activating molecular and physiological changes in plants. This research aimed to determine the effect of B. cereus and B. velezensis on the plant growth and feeding behavior of D. citri. The height and volume of the plant canopy were observed periodically for 6 months, while the feeding behavior of D. citri was monitored using the Electrical Penetration Graph (DC-EPG). The results showed increased height and volume of the citrus plant canopy treated with B. cereus, indicating that B. cereus could act as a PGPR. The application of B. cereus and B. velezensis to citrus seedlings affected the feeding behavior of D. citri. D citri showed difficulty in penetrating the phloem tissue of citrus plants.

Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

Effect of body mass index on post-treatment oral function in patients with oral cancer: a cross-sectional study

This single-center cross-sectional study used sequential sampling to examine the influence of body mass index (BMI) on oral function after oral cancer treatment. Patients who completed primary oral cancer treatment between September 2019 and March 2023 (102 patients, 74 male [72.5%] and 28 female [27.5%]; mean age, 69.6 years) were analyzed. Patient background data were collected from electronic medical records. Post-treatment oral function measurements were conducted on all patients using six assessment tools. Statistical analysis was conducted using Pearson’s correlation coefficient, one-way analysis of variance, the Jonckheere–Terpstra test, and multiple linear regression. Pre-treatment BMI showed a statistically significant relationship with postoperative oral function, particularly tongue pressure (P = 0.01). While the mean values of the groups showed no significant differences, the Jonckheere–Terpstra test revealed a statistically significant trend toward a stepwise increase in tongue pressure for each BMI group (P = 0.03). Multiple linear regression analysis revealed a statistically significant correlation between tongue pressure and pre-treatment BMI (P < 0.05). Pre-treatment BMI was significantly associated with tongue pressure. Since BMI is a variable factor that can be controlled by nutritional therapy even before treatment, nutritional intervention, weight control, and treatment strategies including reconstructive interventions to maintain tongue pressure may be important in oral cancer treatment.

The impact of the COVID-19 pandemic on dyslipidemia in peritoneal dialysis patients.

The COVID-19 pandemic has had a profound impacted on various aspects of society, including the healthcare system and patient care. In this context, this study aims to evaluate the impact of COVID-19 control strategies on the lipid profile and blood sugar levels of peritoneal dialysis (PD) patients in Taiwan, a crucial focus for understanding the pandemic's influence on individuals with chronic kidney disease (CKD). A retrospective cohort study was conducted, analyzing data from the medical records of 170 PD patients who visited the nephrology division of Taipei Veterans General Hospital in 2021. The generalized estimating equations method was used to analyze the longitudinal data and assess the changes in biomarker levels over different periods. Covariates were taken into consideration in the statistical models. The data were analyzed using R 4.3.0. The study enrolled 70 males (41%) and 100 females (59%), with an average age of 56 years old. Over 12 months in 2021, from the first period (January-April: pre-COVID-19) to the second period (May-August: COVID-19 surge), there was a notable decline in both high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels, and a significant surge in triglyceride (TG) levels. However, total cholesterol (TC) and hemoglobin (HbA1c) levels remained stable. Furthermore, the TG to HDL, TG to LDL, TC to HDL, and TC to LDL ratios were analyzed, revealing a pronounced increase during the second period. Our findings underscore the significant impact of COVID-19 pandemic-related disruptions in the healthcare system and the subsequent management strategies on dyslipidemia in PD patients while not affecting dysglycemia. These results provide valuable insights for healthcare professionals to enhance their strategies and interventions for CKD patients undergoing PD during a pandemic.

Prediction and Control of Broadband Noise Associated with Advanced Air Mobility—A Review

This review presents an overview of advanced air mobility broadband noise (BBN) prediction and control techniques, highlighting significant advancements in various prediction models. Methods such as the semi-empirical Brooks–Pope–Marcolini (BPM) model, analytical Amiet model, and time-domain models based on the FW-H equation have been extensively studied. Machine learning (ML) shows promise in BBN prediction but requires extensive data training and application to noise source mechanisms. Passive control methods, such as leading and trailing edge serrations and blade tip designs, have been partially successful but often compromise the aerodynamic performance. Active control methods, like suction and blowing control, trim adjustments, and dielectric barrier discharge (DBD) plasma actuators, show great potential, with the latter two being particularly effective for reducing BBN in thin propeller structures. Overall, while progress has been made in understanding and predicting BBN, further research is needed to refine these methods and develop comprehensive noise control strategies. These advancements hold significant promise for effective and efficient noise mitigation in future AAM vehicles.