Control Rules Research Articles

Fishery Management Enforcement Gradients to Achieve Fishery Goals

Marine protected areas (MPAs) can allow some fish populations to rebuild within their borders in areas impacted by overfishing, but the effectiveness of reserves is highly dependent on how effectively fishing mortality is controlled, which in turn depends on the level of fishery management implementation. In Cuba’s Gardens of the Queen MPA, the largest in the Caribbean, a variety of fishery management measures have been implemented to ensure the social, economic, and political viability of protecting such a large area. Here, we evaluate the biological response, in terms of fish density and the biomass of commercially valuable and ecologically important reef fish species, to a spatial gradient of fishery management enforcement, in terms of fish density and biomass, of commercially valuable and ecologically important reef fish species. The enforcement gradient is characterized by the level of protection, fishing effort, patrolling effort, distance to the nearest fishing port, and fishing intensity. Fish density and biomass were estimated from visual scuba surveys. Areas with higher levels of enforcement support higher levels of average biomass (up to 1378 kg/ha) and density (up to 2367 indv./ha) of commercially important fishes in comparison to areas with very low or no enforcement (estimates of 757 kg/ha average biomass and 1090 indv./ha average density, respectively). These fish density and biomass levels can serve as proxies in the development of harvest control rules that adjust fishing pressure according to the ratio of fished density or biomass to unfished density or biomass, through the use of the MPA Density Ratio method.

Exploring the corruption‐inefficiency nexus using an endogenous stochastic frontier analysis

AbstractSeveral scholars report that, due to reverse causality, the endogeneity problem makes it difficult to identify the real effect of institutional factors at the local level, such as corruption, on firm efficiency. This study employs a panel stochastic frontier analysis that endogenizes production efficiency and robustly assesses the effect of corruption at the local level on firms' technical efficiency. We analyze a large panel of Italian firms operating in the building sector from 2013 to 2019 showing that the determinants of local institutional quality affects firms' performance, with the rule of law and control of corruption playing a preeminent role. Controlling for endogeneity, the magnitude of the effects of institutional quality factors at the local level significantly increase. Our findings are robust to alternative IV strategies, alternative specifications of the production function, and the inclusion of other factors that may affect firm efficiency.

An improved Takagi–Sugeon–Kang-based FxLMS algorithm and its active noise control in electric bus driving position

This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.

Bacterial infections and outcomes of inpatients with COVID-19 in the intensive care unit during the delta-dominant phase: the worst wave of pandemic in Iran.

Epidemiological data regarding the prevalence of bacterial multidrug-resistant (MDR) Gram-negative infections in patients with COVID-19 in Iran are still ambiguous. Thus, in this study we have investigated the epidemiology, risk factors for death, and clinical outcomes of bacterial infections among patients with COVID-19 in the intensive care unit (ICU). This retrospective cohort study included patients with COVID-19 hospitalized in the ICU of a university hospital in Iran between June 2021 and December 2021. We evaluated the epidemiological, clinical, and microbiological features, outcomes and risk factors associated with death among all COVID-19 patients. Data and outcomes of these patients with or without bacterial infections were compared. Kaplan-Meier plot was used for survival analyses. In total, 505 COVID-19 patients were included. The mean age of the patients was 52.7 ± 17.6 years and 289 (57.2%) were female. The prevalence of bacterial infections among hospitalized patients was 14.9%, most of them being hospital-acquired superinfections (13.3%). MDR Klebsiella pneumoniae and Staphylococcus aureus were the most common pathogens causing respiratory infections. Urinary tract infections were most frequently caused by MDR Escherichia coli and K. pneumoniae. The overall in-hospital mortality rate of COVID-19 patients was 46.9% (237/505), while 78.7% (59/75) of patients with bacterial infections died. Infection was significantly associated with death (OR 6.01, 95% CI = 3.03-11.92, p-value <0.0001) and a longer hospital stay (p < 0.0001). Multivariate logistic regression analysis showed that Age (OR = 1.04, 95% CI = 1.03-1.06, p-value <0.0001), Sex male (OR = 1.70, 95% CI = 1.08-2.70, p-value <0.0001), Spo2 (OR = 1.99, 95% CI = 1.18-3.38, p-value = 0.010) and Ferritin (OR = 2.33, 95% CI = 1.37-3.97, p-value = 0.002) were independent risk factors associated with in-hospital mortality. Furthermore, 95.3% (221/232) of patients who were intubated died. Our findings demonstrate that bacterial infection due to MDR Gram-negative bacteria associated with COVID-19 has an expressive impact on increasing the case mortality rate, reinforcing the importance of the need for surveillance and strict infection control rules to limit the expansion of almost untreatable microorganisms.

Optimal Design of Volt/VAR Control Rules of Inverters Using Deep Learning

Optimal Design of Volt/VAR Control Rules of Inverters Using Deep Learning

Design and Analysis of the Effect of the Number of Membership Functions on the Accuracy of the Fuzzy Controller in a Sun Tracking System

Purpose: To investigate how the number of rules in the fuzzy controller affects the single-axis solar tracking system's orientation accuracy. Methodology: Design and compare FLC49 and FLC25 controllers, each with different organic rules and functions. To improve the system efficiency by accurately measuring the maximum power point, the controller receives comparison results from the light sensors installed on the solar panels. We study and analyze the controllers, selecting the best one based on the accuracy of the solar panel orientation. Using the FLC output to operate a two-phase SM. Findings: This study developed a sun tracking system using Matlab/Simulink and compared FLC49 and FLC25 controllers. FLC49 performed better in simulations, with lower settling time and overshoot. The number of rules significantly influenced accuracy, and it also showed lower transient ripple magnitudes, accelerating time response. Unique Contribution to Theory, Practice and Policy: The research investigates the effectiveness of rules in a fuzzy controller and recommends the optimal number of windings to achieve precision in controlling a stepper motor. The study explores the use of 49-rule and 25-rule fuzzy logic controllers to control a stepper motor based on LDR sensor inputs. The researchers found that while both controllers had similar steady time error and overshoot, they differed in orientation accuracy, with the 49-roll fuzzy controller being more accurate in orientation. This single-axis solar tracking system optimizes solar energy conversion into electricity.

Robust PI-PD Controller Design: Industrial Simulation Case Studies and a Real-Time Application

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots.

A heterogeneous pore design algorithm for material extrusion additive manufacturing

Material extrusion additive manufacturing offers great potential for customizing matters with complex external contours, and filament diameter-adjustable 3D (FDA-3D) printing strategy provides fresh impetus to create heterogeneous porous structures inside these complex matters. However, the absence of supporting algorithms to implement FDA-3D printing severely hinders its widespread use. In this paper, we develop a heterogeneous pore design (HPD) algorithm aimed at advancing the development of FDA-3D printing for producing heterogeneous porous matters. The HPD algorithm consists of three sub-algorithms: model design, collapse compensation, and fabrication file (G-codes) generation. As proofs of concept, we utilize this algorithm to 3D print radial gradient and letter-embedded gradient materials following specific steps: (1) designing the heterogeneous porous models with collapse compensation in Grasshopper® and displaying them in Rhinocores®; (2) customizing and writing the corresponding G-codes files by following the material extrusion 3D printer's control rules; (3) upgrading a commercial extrusion printer to FDA-3D print the design models via the customized G-codes. Micro-computed tomography-based 3D reconstruction and quantified pore size maps for the fabricated objects demonstrate the high capability of this HPD algorithm. Overall, the HPD algorithm holds the potential to revolutionize material extrusion 3D printers cost-effectively, creating new possibilities for material extrusion of heterogeneous materials.

Neuroadaptive Cooperative Fault-Tolerant Control of Heterogeneous Multiagent Systems Based on Fully Actuated System Approaches.

The leader-following cooperative problem in heterogeneous multiagent systems (HMASs) with unmodeled dynamics and actuator faults is investigated in this article. The HMASs, which include unmanned ground vehicles and unmanned aerial vehicles, are first described using a fully actuated system model (FASM). The FASM, as opposed to the first-order state-space model, preserves the physical significance of original systems and makes it feasible to apply the control rule entirely. In order to approximate unknown system dynamics, novel neuroadaptive laws with few learning parameters are then suggested. To counteract the negative effects of actuator faults, the Nussbaum function and adaptive approach are utilized. In addition, a cooperative fault-tolerant protocol is suggested, wherein consensus errors are uniformly ultimately bounded. The lack of virtual control variables in the proposed protocol reduces its complexity. The theoretical results are then validated by numerical simulations.

Are natural resources a curse, a blessing, or a double-edged sword? Implications for environmental sustainability

Natural resources have a significant impact on economic and political landscapes of developing countries which determines environmental sustainability. This study explores the convoluted connection between governance and natural resources, examining how resource abundance might influence governance quality. The main components of this dynamic that the theoretical framework considers are the Rentier effects, the repression effects, and the obstruction of modernization. This study creates a governance index applying principal component analysis (PCA) on several governance characteristics, such as political stability, rule of law, government efficacy, regulation and control of corruption, to measure governance comprehensively. Natural resource exports, rents, and the differentiation between renewable and non-renewable natural resources are the three proxies used to quantify natural resources, providing a more nuanced view. To calculate the effect of renewable and non-renewable natural resources on governance in developing nations, the econometric methodology uses a dynamic panel model and system GMM. This analysis reveals that the ability of renewable resources to promote inclusive development has a favourable impact on governance. On the other hand, non-renewable resources show a negative correlation, mainly because of their vulnerability to swings in the world price and their propensity to consolidate power and promote corruption. The significance of differentiating between natural resource types is highlighted in this study, along with the possible advantages of renewable resources for governance and the disadvantages of an over-reliance on non-renewable resources. Policymakers, researchers, and practitioners interested in the development and governance difficulties encountered by resource-rich developing nations can benefit greatly from this research's larger and more complex understanding of the link between natural resources and governance.

A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.

Data analysis method for power big data

This paper discusses a data quality analysis and evaluation mode based on power big data. Its basic content is to use the analysis platform template to conduct acquisition control and process rule analysis, and then use the data quality management module to call out the platform database system that realizes the inspection and analysis function, and retrieve and generate inspection data from the entity base. The analysis program will classify, count, sort, and sort the data, The final generated quantitative indicator data reflecting the project implementation status and data quality will be saved in the analysis result table. The analysis result report called from the middle platform can obtain a more detailed data quality analysis and evaluation report reflecting the data quality of various quantitative projects. This paper has improved the intelligent degree of information quality management and evaluation, realized the intelligent control of information service quality, adapted to the needs of large-scale information service management, and completed the quantitative research and evaluation of information completeness, timeliness, accuracy, accuracy and other important parameters.

An Architecture of Enhanced Profiling Assurance for IoT Networks

Attacks launched from IoT networks can cause significant damage to critical network systems and services. IoT networks may contain a large volume of devices. Protecting these devices from being abused to launch traffic amplification attacks is critical. The manufacturer usage description (MUD) architecture uses pre-defined stateless access control rules to allow or block specific network traffic without stateful communication inspection. This can lead to false negative filtering of malicious traffic, as the MUD architecture does not include the monitoring of communication states to determine which connections to allow through. This study presents a novel solution, the enhanced profiling assurance (EPA) architecture. It incorporates both stateless and stateful communication inspection, a unique approach that enhances the detection effectiveness of the MUD architecture. EPA contains layered intrusion detection and prevention systems to monitor stateful and stateless communication. It adopts three-way decision theory with three outcomes: allow, deny, and uncertain. Packets that are marked as uncertain must be continuously monitored to determine access permission. Our analysis, conducted with two network scenarios, demonstrates the superiority of the EPA over the MUD architecture in detecting malicious activities.

Institutional Quality, Monetary Policy and Banking System Stability in Nigeria

The study used a Generalized Method of Moments to examine the effect of six IQ indices (voice and accountability, rule of law, regulatory quality, government effectiveness, corruption control and political stability and absence of violence) and MP tools (monetary policy rate, loan-deposit ratio, lending rate, logarithm of broad money supply and banking system liquidity ratio) on the Z-Score, a proxy for banking system stability. The study found that, among the six MP indices, only corruption control index has a positive and significant effect on bank stability (coefficient = 0.245124, p = 0.0042). Also, out of five MP variables, only the loan-deposit ratio has a significantly positive effect on bank stability (coefficient = 0.0070482, p = 0.0072). The effect of the other IQ and MP indicators are not significant enough for inference purposes. We conclude that IQ affects banking system stability positively and significantly. We also found that MP affects the stability of the banking system positively and significantly. We recommend, among others, that authorities should continue to pursue zero corruption in institutions and that banks should be mandated to improve on the existing anti-corruption measures. Also the Central Bank of Nigeria should continue to maintain the range of loan-deposit ratio that has existed for many years. There is the need to re-appraise other IQ and MP variables with no significant positive effect on banking system stability in Nigeria.

Adding the risk of stock collapse over time to stock assessments and harvest allocation decisions

Abstract Globally, many fisheries have experienced collapse even though most of these fisheries had management plans with harvest control rules and were supported by scientific modelling that explicitly accounted for uncertainty. Recognizing that an informed decision on risks of a stock collapse versus harvest is only possible when the outcomes of the technical measures are described explicitly. We propose that the cumulative probability of stock collapse over a range of harvest levels would provide a perspective of the future consequences of harvesting decisions. Adding to the harvest level negotiations the consideration of how long a fishery should sustain the livelihoods of fishers may provide managers, fishers, and other stakeholders with a more tangible understanding of the risks within the context of precautionary principles in decision-making. We use a time series from the Canadian Cod fishery of the Southern Gulf of St. Lawrence, from which we construct and calibrate a simplified model as an emulator of more comprehensive models to demonstrate the approach. The implications of adding an analysis of the probabilities of stock collapse for a range of harvest levels and using a risk matrix to inform decision-making are discussed for four selected years 1974, 1986, 1993, and 2017.

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human transcripts, our model learned hidden rules of translation control and achieved a near perfect prediction of canonical translation sites across entire human transcriptome. Surprisingly, this model revealed a new role of codon usage in regulating translation termination, which was experimentally validated. We also identified thousands of new open reading frames in mRNAs or lncRNAs, some of which were confirmed experimentally. The model trained with human mRNAs achieved high prediction accuracy of canonical translation sites in all eukaryotes and good prediction in polycistronic transcripts from prokaryotes or RNA viruses, suggesting a high degree of conservation in translation control. Collectively, we present a general and efficient deep learning model for RNA translation, generating new insights into the complexity of translation regulation.

Design and Simulation Implementation of Indoor Lighting Integrated Control System Based on Fuzzy Control

Abstract With the improvement of people’s living standards, higher requirements have been put forward for the quality of the living environment. The progress of science and technology has provided the possibility for the further development of intelligent buildings. As an important component of intelligent buildings, the intelligence level of intelligent lighting is relatively fall behind, especially energy-saving and comprehensive linkage are not considered in the scope of intelligent lighting. According to the characteristics of indoor environment control variables and controlled variables, this paper uses fuzzy control method to realize the comprehensive control of indoor lighting, summarizes the corresponding measures taken by experience into control rules, and constructs a controller to control the complex lighting process instead of people. Through the construction of control rules, the natural lighting is constrained under the full consideration of the influence of temperature and ventilation. The purpose of giving priority to the use of natural lighting and taking into account the overall energy optimization of the system is achieved. The energy-saving idea of combining lighting control with other systems of intelligent building is proposed, and the system performance is simulated and analyzed. It has the advantages of high integration, high degree of intelligence, energy saving and convenient use.

Fuzzy control charts for individual observations to analyze variability in health monitoring processes

In healthcare monitoring, the quality of medical services and patient outcomes are significantly influenced by the unnatural variations emphasizing the importance of effective control and monitoring strategies. In such scenarios the quality is compromised when a shift in the medical process is not detected timely. In this study, we commenced a comprehensive analysis of the variability present in the patient's hematocrit levels. We introduced a novel approach using fuzzy control charts for individual measurements to detect shift within hematocrit levels effectively. For the initial forecasting of hematocrit level variability, we employed the exponential smoothing method by using R software. Following this, we proposed a set of fuzzy control charts intended for individual measurements, including the fuzzy moving average control chart, fuzzy weighted moving control chart, and fuzzy moving range control chart. These control charts are defined with fuzzy control rules, allowing them to analyze and interpret the small shifts in the process precisely. Also, to measure the process's capability for providing a deeper understanding of the processes, fuzzy process capability indices are introduced. A Monte Carlo simulation approach is employed to obtain the performance metrics. Finally, a case study sourced from Kaggle was conducted to evaluate the performance of the proposed fuzzy control charts for assessing hematocrit levels.

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

Nexus of Human Development and Environmental Quality in Low-Income and Developing Countries: Do Renewable Energy and Good Governance Matter?

The relationship between human development and environmental quality has been explored in this study by examining the human-development status and carbon (CO2)-emissions levels of 60 countries from the low, lower-middle, and upper-middle income categories. The roles of renewable energy and some economic and institutional factors such as GDP, the rule of law, regulatory quality, and corruption control have also been investigated to ascertain their impacts on the relationship. The empirical investigations apply the generalized method of moments (GMM), fixed effects (FE), and random effects (RE) methods, and the long-run associations among the variables are investigated by applying the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) techniques. The robust findings support the trade-off relationship between human development and environmental quality in the selected low-income and developing countries. With evidence of an environmental Kuznets’s relationship between economic growth and environmental quality, these findings reveal that the measures pursued to improve human-development status have a contributory impact on CO2 emissions in the selected countries. However, an increased demand for renewable energy, effective enforcement of the rule of law, and improved control over corruption have a mitigating effect on CO2 emissions. The result has also highlighted the policy issues instrumental to increased emissions levels in these countries. Consequently, it is recommended to formulate policies for resolving disparities within the various dimensions of human development while also making deliberate investments in the socio-economic aspects of human development to ensure both sustainable human development and environmental quality.