Rule-based Research Articles

How is cardiac output controlled in a Fontan circulation: an update.

After creating a Fontan circuit, control of the circulation is shifted from the upstream ventricle to the newly created Fontan portal system. This review aims to illustrate that the customary laws of biventricular cardiac output no longer apply and explain why standardized cardiac failure treatment regimens have little or no effect on a failing Fontan patient. A Fontan circulation is, in effect, a circulation in series regulated by the basic rules of any hydrodynamic circuit. A formula is developed that elucidates how flow through the critical bottleneck, and therefore, the whole circuit is controlled. The critical bottleneck in a hydrodynamic model is the prime determinant of flow and obscures other (less critical) bottlenecks. Once relieved, control of flow shifts to the next most significant bottleneck. The available options for improving flow in a hydrodynamic model are identical to those applicable to any dam: tackle the obstruction (the most impactful approach), push harder upstream (the easiest action), or pull/suck more downstream of the bottleneck (the least efficient strategy). In the early stages, the Fontan neo-portal circulation plays a pivotal role in the pathophysiology. The ventricle has little effect and only has an impact at a late stage. The Fontan formula in the present article stands as a valuable tool, aiding physicians in comprehending the pathophysiological and hydrodynamic intricacies of the Fontan circuit within the context of everyday clinical practice.

The current situation of anthrax in the territory of the Russian Federation in the Astrakhan region

In the modern territory of the Russian Federation, anthrax has always been included in the list of the most particularly dangerous infectious diseases. With minimal capabilities of the control services, significant deaths of farm animals and cases of mass human disease from this pathology were recorded annually. The main reason was the failure to comply with the basic rules for the disposal of corpses of animals killed by anthrax due to low literacy and lack of financial opportunities for their high-quality burial and disposal, which led to contamination of the soil of a significant part of the modern territory of the Russian Federation with the causative agent of anthrax. When disposing of fallen cattle and other animals, the formation of a cattle burial ground was envisaged. More often, in reality, "pestilence fields" were obtained, where the remains of dead cattle were on the open surface. It was only in the 50s that the transition to ash burials of cattle was carried out. The current state of accounting for stationary unfavorable points (SNP) and anthrax burials (SYAZ), combined with a decrease in control by the veterinary service, leads to the incidence of anthrax in animals and humans, but it is mainly sporadic in the form of outbreaks of anthrax. In the Russian Federation, such accounting of the unfavorable nosoareal of anthrax is carried out at all levels of supervision, and the necessary information is collected in the form of reference materials, which allows for differentiated planning of preventive measures in different territories. The inventory of permanently disadvantaged anthrax sites in the Russian Federation has ceased to meet existing needs. Modern electronic databases of geographic information systems contribute to the establishment of general patterns of territorial distribution in the Russian Federation and the reasons contributing to the preservation of the activity of the SNP. This allows for the analysis of information, to make the most accurate reviews and forecasts of the development of the epizooological and epidemiological situation.

A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection

In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset.

PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting

Current waste sorting mechanisms, particularly those relying on manual processes, semi-automated systems, or technologies without Artificial Intelligence (AI) integration, are hindered by inefficiencies, inaccuracies, and limited scalability, reducing their effectiveness in meeting growing waste management demands. This study introduces a prototype waste sorting machine that integrates an AI-driven vision system with a Programmable Logic Controller (PLC) for high-accuracy automated waste sorting. The system, powered by the YOLOv8 deep learning model, achieved sorting accuracies of 88% for metal cans, 75% for paper, and 91% for plastic bottles, with an overall precision of 90%, a recall of 80%, and a mean average precision (mAP50) of 86%. The vision system provides real-time classification, while the PLC manages conveyor and actuator operations to ensure seamless sorting. Experimental results in a controlled environment validate the system’s high accuracy, minimal processing delays, and scalability for industrial recycling applications. This innovative integration of AI vision with PLC automation enhances sorting efficiency, reduces ecological impacts, and minimizes labor dependency. Furthermore, the system aligns with sustainable waste management practices, promoting circular economy principles and advancing the Sustainable Development Goals (SDGs).

Hardware-in-the-Loop testing for low-inertia grids

Abstract Extensive testing of various control algorithms using comprehensive testing frameworks enables us to assess their effectiveness in managing system dynamics and maintaining grid stability. In order to validate the algorithms in the real world, testing of the developed applications in an empirical laboratory environment is essential and the testing parameters in the laboratory have to closely resemble real-world conditions. The fundamental aspect of such testing is to monitor and analyze the dynamics and interaction between the various components in synchronous mode, thereby assessing the system’s ability to operate the grid without violations. The experimental test setup and script-based testing explained in this paper helps to develop innovative control algorithms, with a key focus on medium voltage and low voltage grids with low rotating masses. The test platform makes extended use of a Typhoon Real-Time Simulator (RTS). Firstly, it is used for the simulation of the distributed energy resources and the overlying distribution grid as well as the implementation of control algorithms. Secondly, the RTS acts as a control and SCADA system for the whole testing platform. While the physical components provide measurements in regular intervals, the control algorithms and the energy management system are implemented directly in the Typhoon platform. The data acquisition is performed either via programmable logic controllers for the nodal measurements or through faster measurements such as current transformers. This ensures that the developed algorithms are empirically tested and optimized in close to real-world conditions. The control algorithms provide the set points for the individual components, and the results show the advantages of script-based testing in HIL testing to ensure deterministic and reliable operation of innovative grid components.

A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks

Ships often face various risks when sailing at sea, ranging from harsh natural environments to complex traffic conditions. To reduce the impact of these risks on ships and crews, this paper proposes a navigation risk assessment method that integrates computational intelligence (CI) techniques, such as fuzzy logic, with Bayesian networks (BNs) and utility theory. Firstly, a navigation risk assessment system is established using maritime data and expert knowledge, which evaluates risks from a spatial perspective by considering factors such as safeguard and accident conditions across different regions. Secondly, a fuzzy logic-based numerical and expert data transformation method is proposed to derive the prior probabilities of risk factors in BNs. The weighted fuzzy rule base is used to capture the dependencies among the risk factors. Finally, the probability distribution of navigation risk is determined by combining the prior probability and the dependencies, which are converted into risk index values through utility theory. Taking the grid-based navigation risk assessment of the South China Sea as an example, the effectiveness of this method is verified. The results of the study provide theoretical support for navigation risk assessment based on multi-source data and provide a reference for formulate maritime regulatory policies.

Recommendation on the use of protective eyewear in endourological laser procedures.

This work examines the current evidence available regarding the risks of eye injury in endourology laser environments with the aim of providing a consensus recommendation on the appropriate use of protective eyewear. A working group was set up consisting of urological surgeons and laser protection advisors. A literature review was conducted to identify articles relevant to endourology practice and the commonly used lasers, and these were reviewed by the working group. Searches of the medical device fault/reporting databases were also undertaken. A consensus was developed and shared with stakeholders. No reports of eye injuries from Ho:YAG, Tm:YAG, or Thulium Fibre Lasers (TFLs) were identified, although reports of skin burns and equipment-related fires were found. Available evidence suggests that ocular risks in endourology from these lasers are minimal and limited to rare cases of reversible corneal damage. Protective eyewear can further reduce this risk. However, Lasers with wavelengths below 1400 nm pose significant ocular risks, including blindness. Personnel working in an endourology environment using Ho:YAG, Tm:YAG, or TFLs face minimal ocular risks when adhering to established safety procedures, and laser-specific eyewear may not always be essential. This requires an understanding of the hazards and risks and is in accordance with the recommendations herein, which should form the basis for relevant local rules. Protective eyewear remains critical when using visible and near-infrared lasers due to the heightened associated ocular risks.

An efficient strategy for optimizing a neuro-fuzzy controller for mobile robot navigation

Autonomous navigation is one of the key challenges in robotics. In recent years, several research studies have tried to improve the quality of this task by adopting artificial intelligence approaches. Indeed, the neuro-fuzzy approach stands out as one of the most commonly employed methods for developing autonomous navigation systems. Nevertheless, it may encounter problems of accuracy, complexity, and interpretability due to redundancy in the fuzzy rule base, particularly in the fuzzy sets associated with the system’s variables. In this work, a strategy is proposed to optimize an adaptive-network-based fuzzy inference system (ANFIS) controller for reactive navigation by addressing the problem of complexity and accuracy. It consists in combining a suite of methods, namely, data-driven fuzzy modeling, fuzzy sets merging, fuzzy rule base simplification, and parameter training. This process has produced a fuzzy inference system-based controller with high accuracy and low complexity, enabling smooth and near-optimal navigation. This system receives local information from sensors and predicts the appropriate kinematic behavior that enables the robot to avoid obstacles and reach the target in cluttered and previously unknown environments. The performance of the proposed controller and the efficiency of the followed strategy are demonstrated

Control System Design for 3 Storey Elevator Using PLC OPTA FINDER

Elevators are a crucial vertical transportation system in multi-story buildings, and the use of an appropriate control system can enhance operational efficiency, comfort, and safety. This study aims to design a control system for a 3-floor elevator using a Programmable Logic Controller (PLC) OPTA FINDER, which offers advantages in flexibility and ease of programming. The design process involved creating a control system schematic, programming with ladder diagrams, and testing on a small-scale elevator model. The test results demonstrated that the system could operate the elevator with fast and accurate response in detecting floor positions, controlling automatic doors, and responding to emergency conditions. Furthermore, the PLC OPTA FINDER proved reliable in managing elevator system components and facilitating program modifications without requiring hardware changes. This control system also contributes to energy efficiency through optimized motor speed regulation. Thus, the implementation of the PLC OPTA FINDER provides an effective and safe solution for modern elevator control systems.

Automatic Bottle Filling Unit using PLC

This research paper explores the design and implementation of an automatic bottle filling system controlled by a Programmable Logic Controller (PLC). The main objective of this study is to improve the efficiency and precision of filling operations, particularly in industries such as beverage production, pharmaceuticals, and chemicals. The system aims to replace the traditional manual bottling processes, which often suffer from human error, inconsistencies, and inefficiency. By integrating various sensors, actuators, and a PLC-based control system, this automated filling unit enhances speed, reduces waste, and ensures precise filling to the required levels. The PLC coordinates all the activities, including bottle detection, liquid dispensing, and capping, contributing to an optimized and error-free production process.

An online ultrasonic flowmeter fault diagnosis method based on an extended belief rule base

An online ultrasonic flowmeter fault diagnosis method based on an extended belief rule base

Forgetting in Abstract Argumentation: Limits and Possibilities

The topic of forgetting, which loosely speaking means losing, removing, or even hiding some variables, propositions, or formulas, has been extensively studied in the field of knowledge representation and reasoning for many major formalisms. In this article, we convey this topic to the highly active field of abstract argumentation. We provide an in-depth analysis of desirable syntactical and/or semantical properties of possible forgetting operators. In doing so, we included well-known logic programming conditions, such as strong persistence or strong invariance. Further, we argue that although abstract argumentation and logic programming are closely related, it is not possible to reduce forgetting in abstract argumentation to forgetting in logic programming in a straightforward manner. The analysis of desiderata, adapted to the specifics of abstract argumentation, includes implications among them, individual and collective satisfiability, and identifying inherent limits for a set of prominent semantics. Finally, we conduct a case study on stable semantics incorporating concrete forgetting operators.

Controlled Language Increases Comprehension of Law for People

Law is written in a way that is not easily understandable by most people. This also hinders the translation into computer code, which would permit tools that increase access to the law. The use of controlled language in legal texts is a promising way to implement laws that are better comprehensible by people, and computer-executable at the same time. However, while the scholarship on controlled language has been concentrating on its automatic translation into (logic) programs, the effects on people have not been investigated. We conducted an experiment to test the understandability, usability, and perceived open-texture (e.g., ambiguity, vagueness) of legal texts that are formulated in natural language and in controlled language. Among our 73 participants—including members of parliament (MPs) and public servants—we found that the use of controlled language increases understandability across legal domains and increases the number of terms that are perceived as open-texture. We also found that public servants show higher comprehension of legal statutes but lower perception of open-texture than MPs, regardless of whether these are in controlled or natural language. We conclude that controlled language can therefore support a wider access to law, thereby bringing democratic societies closer to their underpinning ideals and principles.

The Rutherford-Harkins-Landau-Chadwick Key–I. Introduction to Nuclear Chemistry

For organic chemistry, Kekulè ´s theory of the structure of benzene provided dramatic new clarity of understanding and a reliable guide to both analytic and synthetic studies. The field of organic chemistry developed explosively from this point. Many new models of atomic nuclei have been developed during the last hundred years, and they have advantages and disadvantages. However, no nucleus model based on unorganized structures of protons (usually depicted as red balls) and neutrons (usually depicted as blue balls) can offer a simple predictive model to describe and predict the results of fusion and fission reactions. Many experimental data in nuclear physics during the past century should be newly organized. Is it possible to discover a hidden key that opens doors to a deeper understanding of events occurring in the femtometer size scales? In our model, we propose returning to the bifurcation point in nuclear physics that Pauli and Fermi determined with their neutron and neutrino model in 1934. Based on the classical nuclei models proposed by Old Masters–Rutherford, Harkins, Landau, and Chadwick-before the year 1934, we attempt to further develop their models based on the compound neutron (the composition of proton and electron). Several basic rules for the nuclear structure were postulated, leading to a new view of the world of nuclei. The potential of this model will be documented in three interconnected papers.

Swarm Intelligence in Multiplexed Robots Problems Identified

Although kindling with the very consciousness of the human mind may have been banned, this did not stop scientists from tinkering and innovating with all the technology they were surrounded by. Stumbled by mathematical problems, mathematicians developed certain tactics we call algorithms. Algorithms are the layman's logic language for any device to function according to the desired task.Well, Mother Nature has already built us an unimaginable and incomprehensible algorithm propelled by personal choices, emotions, feelings, and sometimes rationality. For people who still didn't get it, we call it brain in day-to-day language. Our mind subconsciously designs such algorithms for us to efficiently function in our every day to day lives. Our project is an effort to reconstruct such rationality of thinking by artificial means through the use of reinforcement learning and robots to conclude the following 1) Is it possible to recreate human understanding? 2) If yes, then how do we channel it into something practical and physical? 3) How do we utilize this so-formed machine as a result of the conclusion of the second question? Asking this to ourselves we pursued to make our project P3 Hexa which is an endeavor to tinker with swarm intelligence and physically experience the functioning of a self-designed algorithm

A new method for assessing the health status of aerospace equipment based on a belief rule base with balanced accuracy and complexity

The health status of aerospace equipment directly affects the operational capability of the entire system. Belief rule base (BRB) is an effective method for assessing health status that combines expert knowledge and historical data. However, in the actual assessment, the data provided by experts only form the basic framework of the model. Therefore, the BRB model with joint optimization of structure and parameters (BRB-SPO) is proposed to achieve a balance between the model’s accuracy and complexity. First, to balance complexity and accuracy of the model, parameter structure backward stepwise selection method (BSS) and full factorial design (FFD) are introduced. BSS constructs the optimal parameter set, while FFD determines the best parameter values for the model. Subsequently, the constructed model is deduced using the evidential reasoning (ER) calculation procedure, the other parameters are optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). Finally, the practicality of the proposed method is validated through two examples.

Alat Penghancur Kulit Kayu Manis Berbasis PLC

This study presents the design and development of an automated cinnamon bark shredder control system using a Programmable Logic Controller (PLC). The purpose of the system is to enhance production efficiency by replacing manual processes with automation, thereby reducing time, labor costs, and error rates. The system integrates components such as proximity sensors, induction motors, and PLC Omron CP1E, programmed with the CX-Programmer software. Testing demonstrated that the shredder effectively processes cinnamon bark into fine powder meeting Indonesian National Standards (SNI) for export, with consistent results in particle size (0.7–1 cm) and reduced waste. The tool is suitable for small and medium-sized industries (IKM) due to its cost efficiency, operational simplicity, and ability to improve production capacity. Future developments may focus on scaling up for industrial use.

Impact of Traffic Park Use on Children’s Traffic Rule Awareness and Behavioral Intentions: Case Study in Toyohashi City

To ensure children’s safe independent mobility on the road, they need to learn basic traffic rules. In this case, traffic rule education in a realistic environment through a play-way method can be a significant learning strategy. This research focuses on the “Traffic Park (TP)”, which provides that opportunity. Specifically, this research examined how elementary school children’s knowledge of traffic rules and behavioral intentions are influenced by their experiences of using and playing in a TP before and after they start school. Children from four different elementary schools (grades 1~6) were surveyed in Toyohashi City, Japan using a web-based questionnaire survey. Structural equation modeling (SEM) was applied to analyze the effect of TP use experience on children’s awareness of traffic rules and behavioral intentions. Three distinct SEM models were tested to measure the effect pattern of children’s TP use experience on their traffic rule awareness and behavioral intentions. The results show that TP use experience before entering school has a statistically significant effect on children’s traffic rule awareness (β = 0.16, p < 0.004; model 1) and behavioral intention improvement (β = 0.09, p < 0.07; model 2). However, TP use experience after entering school was found to have no significant effect. Finally, children’s TP use experience indirectly improves their behavioral intentions (β = 0.74, p < 0.001; model 3) by improving their awareness of traffic rules. Overall, the findings of this study highlight the contribution of TPs in promoting children’s safe independent mobility and fostering the development of sustainable child-friendly cities worldwide.

Design and Implementation of a Smart Fixture Machine for Accurate Hole Punching and De-gating in Plastic Injection Molding

This paper presents the design and implementation of a unique semi-automatic fixture machine tailored to optimize the de-gating and hole punching process in plastic injection molding. Addressing challenges such as pin breakage, burr formation, and inefficiencies in secondary operations, the newly developed system integrates laser beam sensors and programmable logic control (PLC) for precise part positioning verification. This innovative solution reduced cycle time by 40% and eliminated secondary labor and cost, demonstrating significant advancements in smart manufacturing. Keywords: Plastic Injection Molding, Fixture Machine, Hole Punching, De-gating, Laser Beam Sensor, Cycle Time Optimization, Smart Manufacturing, Automation.

Léon Péan de Saint-Gilles

Léon Péan de Saint-Gilles (1832-1863) was a French chemist, without a formal education, who studied the allotropy of sulfur and the properties and reactions of the different species, the oxidizing properties of potassium permanganate, the oxidation of copper under the simultaneous influence of air and ammonia, and the action of cyanogen on acetaldehyde. With Marcellin Berthelot they determined the basic rules of the esterification of an acid by an alcohol in its many routes.