Rule-based Research Articles

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.

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.

Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models Through Question Answering from Text to Video

Understanding sports presents a fascinating challenge for Natural Language Processing (NLP) due to its intricate and ever-changing nature. Current NLP technologies struggle with the advanced cognitive demands required to reason over complex sports scenarios. To explore the current boundaries of this field, we extensively evaluated mainstream and emerging large models on various sports tasks and addressed the limitations of previous benchmarks. Our study ranges from answering simple queries about basic rules and historical facts to engaging in complex, context-specific reasoning using strategies like few-shot learning and chain-of-thought techniques. Beyond text-based analysis, we also explored the sports reasoning capabilities of mainstream video language models to bridge the gap in benchmarking multimodal sports understanding. Based on a comprehensive overview of main-stream large models on diverse sports understanding tasks, we presented a new benchmark, which highlighted the critical challenges of sports understanding for NLP and the varying capabilities of state-of-the-art large models on sports understanding. We also provided an extensive set of error analyses that pointed to detailed reasoning defects of large model reasoning which model-based error analysis failed to reveal. We hope the benchmark and the error analysis set will help identify future research priorities in this field.

A Spatiotemporal Fuzzy Modeling Approach Combining Automatic Clustering and Hierarchical Extreme Learning Machines for Distributed Parameter Systems

Modeling distributed parameter systems (DPSs) is challenging due to their strong nonlinearity and spatiotemporal coupling. In this study, a three-dimensional fuzzy modeling method combining genetic algorithm (GA)-based automatic clustering and hierarchical extreme learning machine (HELM) is proposed for DPS modeling. The method utilizes GA-based automatic clustering to learn the premise part of 3D fuzzy rules, while HELM is employed to learn spatial basis functions and construct a complete fuzzy rule base. This approach effectively captures the spatiotemporal coupling characteristics of the system and mitigates the information loss commonly observed in dimensionality reduction in traditional fuzzy modeling methods. Through experimental verification, the proposed method is successfully applied to a rapid thermal chemical vapor deposition system. The experimental results demonstrate that the method can accurately predict temperature distribution and maintain good robustness under noise and disturbances.

Health state assessment method for complex system based on multiexpert joint belief rule base

The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results.

Of “Employer Skills” and “Poetry”: Logics of New Arts and Humanities Programs

Of “Employer Skills” and “Poetry”: Logics of New Arts and Humanities Programs

Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber-Physical Robotic System-Digital Twin Approach.

This paper deals with a "digital twin" (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber-physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC. In addition to the connection via Profibus of the four PLCs, related to the WSs, to the main PLC, there are also the connections of other devices to the local networks, LAN Profinet and LAN Ethernet. There are the connections to the Internet, Cloud and Virtual Private Network (VPN) via WAN Ethernet by open platform communication unified architecture (OPC-UA). The overall system follows a DT approach that enables task planning through augmented reality (AR) and uses virtual reality (VR) for visualization through Synchronized Hybrid Petri Net (SHPN) simulation. Timed Petri Nets (TPNs) are used to control the processes within the MPS's workstations. Continuous Petri Nets (CPNs) handle the movement of the MCPRS. Task planning in AR enables users to interact with the system in real time using AR technology to visualize and plan tasks. SHPN in VR is a combination of TPNs and CPNs used in the virtual representation of the system to synchronize tasks between the MPS and MCPRS. The workpiece (WP) visits stations successively as it is moved along the line for processing. If the processed WP does not pass the quality test, it is taken from the last WS and is transported, by MCPRS, to the first WS where it will be considered for reprocessing or scrapping.

Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study.

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.

A System Control and Waveform Acquisition Framework for the Kicker System at SHINE

A lumped kicker magnet system is being developed as part of the electron beam switchyard of the Shanghai High Repetition Rate X-Ray Free-Electron Laser (XFEL) and Extreme Light (SHINE) facility at Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS). SHINE is a linac-based facility with an energy of 8 GeV and a repetition rate of 1 MHz for multiple users. The kicker system is a critical element of the deflection system and determines the stability performance of the beamlines. To ensure autonomous and reliable operation in a harsh environment, a dedicated control system—consisting of a custom-designed programmable logic controller (PLC) and waveform acquisition system—has been developed based on the Experimental Physics and Industrial Control System (EPICS). The PLCsystem enables status acquisition, control implantations, and alarm handling, allowing for automated operation. The waveform acquisition system provides an intuitive demonstration and enables the real-time diagnosis of kicker waveforms. The results demonstrate a half quasi-sinuous waveform with a peak current of 11.5 A and a full width at half maximum of 218 ns at a repetition rate of 10 kHz. The detailed design, overall concept, achieved performance, and initial test results of two modular controls are provided for further demonstration.

Popper and Lakatos on what is distinctive about empirical science

ABSTRACT This article offers critical analysis of the debate between Karl Popper and Imre Lakatos about distinctive features of empirical science that allow to demarcate it from non-science and especially pseudoscience. Popper's demarcational proposition is explained as involving essentially two requirements, logical (to the theory itself, that it had potential falsifiers) and methodological (to scientists' attitude with respect to the theory, that they were willing to recognise its empirical falsification. Lakatos's criticisms of ‘Popper's basic rule’ that ‘criteria of refutation have to be laid down beforehand’ and Popper's reply to these criticisms, as well as Lakatos's alternative demarcational proposition, are discussed. The argument is made that although Lakatos's criticisms and proposition have considerable merits, he goes too far in claiming that it is OK for scientists to wave away or ignore apparent falsifications of a theory so long as the theory is successful in generating lots of successful unexpectable predictions, and generally understates the importance of the critical attitude for the development of science. On the other hand, Popper's falsificationist approach can be modified so as to accommodate sound points of Lakatos's criticisms and proposition while retaining the basic structure and critical rationalist character of Popper's demarcational proposition.

An AI-Driven Approach to Automatic Code Analysis and Summarization for Enhanced Software Understanding

- The AI-Driven Code Analyzer is an innovative tool designed to address the challenges developers, students, and educators face in understanding complex code. With programming becoming an integral part of various industries, the need for accessible tools that simplify code comprehension has never been greater. This tool allows users to input code and receive detailed, step-by-step explanations of its functionality, helping to demystify programming logic and enhance understanding. Whether it’s a beginner struggling with foundational concepts or an experienced programmer dealing with an unfamiliar codebase, the AI-Driven Code Analyzer serves as a valuable resource for improving productivity and learning efficiency. At the heart of this tool lies its integration with the Gemini API, a sophisticated backend system that processes inputted code and generates human-readable explanations. This integration ensures a seamless and efficient user experience, providing accurate and insightful breakdowns of even the most complex algorithms. The modular design of the platform allows it to handle diverse programming languages and concepts, making it a versatile solution for various user needs. Complementing its technical prowess is a user-friendly interface that simplifies interaction, enabling users to focus solely on understanding the code rather than navigating the tool itself. The applications of the AI-Driven Code Analyzer extend far beyond individual use. In educational settings, it can be leveraged as a teaching aid, helping students grasp difficult programming concepts through real-world examples. In professional environments, it can assist developers in debugging, code reviews, and understanding legacy systems, significantly reducing time spent on manual analysis. By streamlining the process of code explanation, this tool not only fosters a deeper understanding of programming but also addresses the growing demand for accessible and intelligent software solutions in an increasingly tech-driven world. Key Words: Code Analysis, Code Summarization, Code Comprehension, Human-Readable Explanations, Natural Language Processing (NLP), Programming Logic, Collaborative Development, Productivity Enhancement, Educational Applications, System Architecture, Learning Efficiency

A Classification Model Based on Interval Rule Inference Network with Interpretability

Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement.

Identifying Needs and Support Services for Family Caregivers of Older Community-Based Family Members: Mixed-Method Research Findings.

A recent Canadian study conducted in one province identified family caregiver support needs and essential support services when caring for older community-based family members requiring assistance with activities of daily living. Weekly interviews of 150 volunteer caregivers over 6 months identified 11 support needs and 5 essential support services. Scoping literature reviews of the 11 needs found they had all been identified before. Program logic investigations of the 5 support services identified a patch-work of temporarily available support services in existence across the province. Two governmental policies are recommended: (a) provincial policy assuring access to the five support services, and (b) federal policy for federal-provincial funding transfers to address the provincial cost of assured community-based support services. Family caregivers require this support to maintain their own and their family member's well-being, particularly as this caregiving prevents or delays older family member hospitalizations and nursing home entry.

Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing.

Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. In the present report, we addressed these limitations by developing Programmable Enrichment via RNA FlowFISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations defined by the abundance of specific RNA transcripts. Across immune populations (n = 184,126 cells) and fresh-frozen and formalin-fixed, paraffin-embedded brain tissue (n = 33,145 nuclei), we demonstrated that programmable sorting logic via RNA-based cytometry can isolate rare cell populations and uncover phenotypic heterogeneity via downstream, high-throughput, single-cell genomics analyses.

Program Logics à la Carte

Program logics have proven a successful strategy for verification of complex programs. By providing local reasoning and means of abstraction and composition, they allow reasoning principles for individual components of a program to be combined to prove guarantees about a whole program. Crucially, these components and their proofs can be reused. However, this reuse is only available once the program logic has been defined. It is a frustrating fact of the status quo that whoever defines a new program logic must establish every part, both semantics and proof rules, from scratch. In spite of programming languages and program logics typically sharing many core features, reuse is generally not available across languages. Even inside one language, if the same underlying operation appears in multiple language primitives, reuse is typically not possible when establishing proof rules for the program logic. To enable reuse across and inside languages when defining complex program logics (and proving them sound), we serve program logics à la carte by combining program logic fragments for the various effects of the language. Among other language features, the menu includes shared state, concurrency, and non-determinism as reusable, composable blocks that can be combined to define a program logic modularly. Our theory builds on ITrees as a framework to express language semantics and Iris as the underlying separation logic; the work has been mechanized in the Coq proof assistant.

Finite-Choice Logic Programming

Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints—there is no single canonical model. The notion of stable models for logic programs with negation has successfully captured programmer intuition about the set of valid solutions for such problems, giving rise to a family of programming languages and associated solvers known as answer set programming. Unfortunately, the definition of a stable model is frustratingly indirect, especially in the presence of rules containing free variables. We propose a new formalism, finite-choice logic programming, that uses choice, not negation, to admit multiple solutions. Finite-choice logic programming contains all the expressive power of the stable model semantics, gives meaning to a new and useful class of programs, and enjoys a least-fixed-point interpretation over a novel domain. We present an algorithm for exploring the solution space and prove it correct with respect to our semantics. Our implementation, the Dusa logic programming language, has performance that compares favorably with state-of-the-art answer set solvers and exhibits more predictable scaling with problem size.