Search Results (41)

Professional learning communities (PLCs) are widely researched and of growing interest internationally. In Oman, some research has been started at the school level. However, at the time of this study, no research had been conducted at the higher education (HE) level. Hence, the study took place at an Omani university through an action research project lasting one semester. It aimed at establishing and evaluating a PLC to understand the first-hand experiences of the members of this community. The study is based on the sociocultural theory of Vygotsky, which stresses that learning is social. Also, the study used interpretivism and social constructivism to deeply analyse members’ interactions and perceptions of the PLC. Data were collected via preliminary documentary analysis of the reports produced by Staff Development Committee, observations of PLC meetings, and semistructured interviews during and at the end of the semester. The findings showed positive attitudes towards the PLC, where the members could sense a supportive learning environment. They were happy sharing their classroom practices, challenges, reflections, and learning from one another. Overall, they found professional development (PD) sessions fruitful, and they encouraged establishing a PLC along with the current PD programme because the PLC directly spotlighted their needs. Although the members indicated the potential of creating a sustainable PLC, their participation was challenged by factors (e.g., workload, time constraints, and technical issues). The members suggested many solutions to make the PLC a successful learning experience. Implications for policymakers and educators were drawn from the findings. Full article

Regarding the issue of abnormal data mining of electricity meters in the PLC application area, an intelligent measurement network architecture integrating protocol message interaction and an anomaly detection module has been designed. Based on an improved convolutional neural network (ICNN), abnormal messages during the transmission and reception process are monitored to enhance the reliability of power information collection data. Firstly, common anomalies during the meter reading operation are analyzed using protocol analysis tools, including abnormal power data, excessive delay, message out of order, etc. Subsequently, a dataset containing these anomalies with a preset proportion is constructed, and through data splicing and matrix processing, it is transformed into a two-dimensional image set to optimize the recognition effect of the convolutional neural network. Ultimately, an anomaly detection algorithm based on the ICNN is developed. Gray wolf optimization (GWO) is adopted to improve the algorithm’s performance, and the algorithm is integrated into the anomaly detection module. The experimental results demonstrate that, compared with the CNN-LSTM and CNN-SVM algorithms, the proposed algorithm offers an advantage in terms of complexity while achieving an accuracy rate of 98.8%, providing a reliable anomaly detection solution for metering network measurement systems. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

Joint acidosis is increasingly recognized as an important determinant of cellular behavior in osteoarthritis (OA). Declines in extracellular pH (pHe) occur across cartilage, meniscus, synovium, and subchondral bone, where they influence inflammation, matrix turnover, and pain. Among proton-sensing G protein-coupled receptors, GPR68 responds to the acidic pH range characteristic of human OA joints. The receptor is activated between pH 6.8 and 7.0, couples to Gq/PLC-MAPK, cAMP-CREB, G12/13-RhoA-ROCK signaling pathways, and is expressed most prominently in articular cartilage, with additional expression reported in synovium, bone, vasculature, and some neuronal populations. These pathways regulate transcriptional programs relevant to cartilage stress responses, inflammation, and matrix turnover. GPR68 expression is increased in human OA cartilage and aligns with regions of active matrix turnover. We previously reported that pharmacologic activation of GPR68 suppresses IL1β-induced MMP13 expression in human chondrocytes under acidic conditions, indicating that increased GPR68 expression may represent a microenvironment-responsive, potentially adaptive signaling response rather than a driver of cartilage degeneration. Evidence from intestinal, stromal, and vascular models demonstrates that GPR68 integrates pH changes with inflammatory and mechanical cues, providing mechanistic context, although these effects have not been directly established in most joint tissues. Small-molecule modulators, including the positive allosteric agonist Ogerin and the inhibitor Ogremorphin, illustrate the tractability of GPR68 as a drug target, although no GPR68-directed therapies have yet been evaluated in preclinical models of OA. Collectively, current data support GPR68 as a functionally relevant proton sensor within the acidic OA joint microenvironment. Full article

Reliable positioning performance is crucial in precision industrial automation, especially under dynamic conditions. This research focuses on examining the accuracy of a toothed belt driven linear servo motor positioning system, with the aim of identifying the main factors influencing position deviation. The system was built on a Power Belt ITO 060M shaft, controlled by an Rtelligent RS200-G servo controller and an Omron CP1L-E PLC. Position measurement was performed by a laser distance meter and a Cognex IS2000C-130-40-SR8 industrial camera, both calibrated with certified gauge blocks. The linear unit was moved to predefined points at different speeds, accelerations, and decelerations profiles and the resulting position deviation was recorded for each case. Several analytical methods were used to evaluate the collected measurement data to determine which factors have the greatest impact on positioning error. The result showed that speed significantly affected the accuracy of the system, while the effects of deceleration and acceleration were less pronounced. The study contributes to the fine-tuning of linear motion system and the targeted improvement of their performance. Full article

Background/Objectives: This study examined how maker-based physical education (PE) lessons, co-designed within a Professional Learning Community (PLC), expanded student participation and supported teacher professional growth. Focus was placed on engaging sport-marginalized students, often excluded due to ability, motivation, or social background. Methods: This qualitative single-case study examined a PE-focused professional learning community (PLC) that collaboratively designed maker-based PE lesson prototypes and partially implemented them in regular PE classes. Data included PLC documents, lesson plans, classroom observations, student work, and semi-structured teacher interviews, and were analyzed using inductive category analysis. Results: Three lesson types emerged: (1) physical data measurement and analysis, (2) performance feedback, and (3) play- and game-based formats. These diversified participation by promoting student roles beyond performers, such as creators and analysts. Sport-marginalized students took on new roles as creators and analysts and, at the same time, showed increased engagement in physical activities and more active participation in lessons as performers. Teachers shifted from skill-focused instruction to reflective, practice-based teaching. The PLC enabled sustained innovation and collective growth. Conclusions: Maker-based PE offers a low-cost, adaptable model for inclusive curriculum reform that promotes creativity, wellbeing, and participation. Future studies should explore its long-term impact, broader implementation, and strategies to support ongoing PLC-based innovation. Full article

With the rapid development of electric vehicles, the issue of wiper–windshield friction noise has become more prominent. However, limitations in the hardware and software configurations of existing experimental systems restrict in-depth studies of frictional vibration and noise mechanisms. This study develops an experimental system with functions for working condition adjustment, data acquisition, and analysis of wiper–windshield frictional vibration and noise. First, the overall design of the wiper–windshield experimental system is described. The system allows adjustment of the motion gear and friction coefficient and facilitates data collection and analysis of pressure, vibration, and noise. The design includes the mechanical structure, electronic and electrical components, and software system of the experimental setup. A PLC control program (lower computer) and human–computer interaction software (upper computer) based on LabVIEW are developed to drive and control the mechanical structure, enabling working condition adjustment, data acquisition, and analysis. Finally, an experimental scheme is implemented to verify the feasibility of the wiper–windshield experimental system. Mechanical property, vibration, and noise data from the wiper are collected by simulating the operating conditions of a real vehicle. The experimental results demonstrate that the designed wiper–windshield experimental system can adjust various working conditions and support the collection and analysis of diverse data, facilitating theoretical research on the generation mechanism, influence rules, and control methods for wiper–windshield frictional vibration and noise. Full article

With the advent of Industry 4.0, the manufacturing industry is facing unprecedented data challenges. Sensors, PLCs, and various types of automation equipment in smart factories continue to generate massive amounts of heterogeneous data, but existing systems generally have bottlenecks in data collection standardization, real-time processing capabilities, and system scalability, which make it difficult to meet the needs of efficient collaboration and dynamic decision making. This study proposes a multi-level industrial data processing framework based on edge computing that aims to improve the response speed and processing ability of manufacturing sites to data and to realize real-time decision making and lean management of intelligent manufacturing. At the edge layer, the OPC UA (OPC Unified Architecture) protocol is used to realize the standardized collection of heterogeneous equipment data, and a lightweight edge-computing algorithm is designed to complete the analysis and processing of data so as to realize a visualization of the manufacturing process and the inventory in a production workshop. In the storage layer, Apache Kafka is used to implement efficient data stream processing and improve the throughput and scalability of the system. The test results show that compared with the traditional workshop, the framework has excellent performance in improving the system throughput capacity and real-time response speed, can effectively support production process judgment and status analysis on the edge side, and can realize the real-time monitoring and management of the entire manufacturing workshop. This research provides a practical solution for the industrial data management system, not only helping enterprises improve the transparency level of manufacturing sites and the efficiency of resource scheduling but also providing a practical basis for further research on industrial data processing under the “edge-cloud collaboration” architecture in the academic community. Full article

In this article, we propose a fuzzy proportional–integral–derivative (Fuzzy-PID) controller that integrates a system-identification-based control strategy. We aim to address the challenge of regulating electrical conductivity (EC) in a fertigation system to ensure precise nutrient delivery. During fertilization, the nutrient solution EC value increases gradually and nonlinearly as water and fertilizer are integrated. Precise fertilizer injection is essential to maintain stable EC levels, preventing crop undernutrition or overnutrition. The fertigation process is modeled using a particle swarm optimization (PSO)-based system identification method. A Fuzzy-PID method is then employed to regulate the nutrient solution EC value based on the pre-determined or real-time identified transfer model. The proposed control strategy is deployed within a programmable logic controller (PLC) environment and validated on a PLC-based fertilizer system. The results show that the identified transfer model accurately represents the fertilizer mixing process, achieving a standard Mean Absolute Percentage Error (MAPE) value of less than 5% within 2 s using the proposed PSO-based identification method. In the simulation tests, the proposed Fuzzy-PID control rule would converge the nutrient solution to target EC values 1000 and 1500 μs/cm within a deviation band ± 50 μs/cm, within 6 s from the recorded identified transfer models and within 25 s from the real-time identified transfer models. In the device’s test, the convergence time of the fertigation EC control is approximately 16 s from the history data and 42 s from the real-time collected data, with a deviation band ± 50 μs/cm. In contrast, it may take over 70 s for the EC regulation of the same fertilization, using the classic control methods including conventional PID and Fuzzy-PID. The proposed control strategy significantly improves EC regulation in terms of speed, stability, and precision, enhancing the performance of fertilizer mixing systems. Full article

Industrial control systems (ICSs) are a critical component of key infrastructure. However, as ICSs transition from isolated systems to modern networked environments, they face increasing security risks. Traditional anomaly detection methods struggle with complex ICS traffic due to their failure to fully utilize both low-frequency and high-frequency traffic information, and their poor performance in heterogeneous and non-stationary data environments. Moreover, fixed threshold methods lack adaptability and fail to respond in real time to dynamic changes in traffic, resulting in false positives and false negatives. To address these issues, this paper proposes a deep learning-based traffic anomaly detection algorithm. The algorithm employs the Hilbert–Huang Transform (HHT) to decompose traffic features and extract multi-frequency information. By integrating feature and temporal attention mechanisms, it enhances modeling capabilities and improves prediction accuracy. Additionally, the deep probabilistic estimation approach dynamically adjusts confidence intervals, enabling synchronized prediction and detection, which significantly enhances both real-time performance and accuracy. Experimental results demonstrate that our method outperforms existing baseline models in both prediction and anomaly detection performance on a real-world industrial control traffic dataset collected from an oilfield in China. The dataset consists of approximately 260,000 records covering Transmission Control Protocol/User Datagram Protocol (TCP/UDP) traffic between Remote Terminal Unit (RTU), Programmable Logic Controller (PLC), and Supervisory Control and Data Acquisition (SCADA) devices. This study has practical implications for improving the cybersecurity of ICSs and provides a theoretical foundation for the efficient management of industrial control networks. Full article

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication—Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed. Full article

The article describes solutions in the field of diagnostics of a control system based on a CNC and the cooperation with an industrial robot. The industrial robot is controlled directly from the CNC. Data exchange between the CNC and the robot controller allows for collecting the most important process data from the robot. Then, calculations are performed in the PLC using a number of functions to obtain consumption indicators of individual robot components. The data were visualized on the HMI screens of the CNC. Additionally, a dedicated interface was prepared to share these data using the MQTT protocol for IIoT solutions. The entire solution was implemented and then deployed in a real station. The presented solution is an extension of the possibilities of operating an industrial robot by CNC towards diagnostics and early failure prevention. Full article

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats. Full article

During this research, an automatic monitoring system was developed to monitor the working parameters in a solar power plant consisting of two flexible silicon modules. The first stage of the monitoring system relies on a microcontroller, which collects data from wattmeter modules made using a microcontroller. This tier also includes DC/DC converter and RS232-TCP converter modules for data transfer. The second stage, the industrial PLC, receives data from the first stage and transmits them to the PC, where the information is stored and the processes are visualized on the HMI screen. During this study, the charging process was analyzed using PWM- and MPPT-type charging controllers, as well as the power supply of Fito LED strips for lighting plants. Using the created monitoring system, the parameters of the solar power plant with flexible PV modules were monitored. This study compared PWM and MPPT battery charging methods, finding that MPPT is more efficient, especially under unstable solar conditions. MPPT technology optimizes energy usage more efficiently, resulting in faster battery charging compared to PWM technology. Full article

In this study, we propose an intelligent drinking water controller based on programmable logic controller (PLC) specifically designed for pig breeding, which significantly reduces the water waste caused by the use of traditional drinking bowls by regulating the frequency and flow of water release. In addition, the drinking water system has a tracking and recording function, which can record the frequency and duration with which fattening pigs drink water in each pen in detail, thus providing farmers with a wealth of pig health and behavior data to help optimize breeding management decisions. In order to deeply analyze the effects of the intelligent drinking water controller on the growth, resources environment and economic benefits of fattening pigs under the condition of large-scale breeding, a single factor comparison experiment was designed.In this experiment, 84 fattening pigs were selected and distributed in 12 pens. Among them, six pens were randomly designated as the control group;the pig in this group used ordinary drinking water bowls for the water supply. The other six pens were designated as the experimental group;the pigs in this group used the intelligent drinking water controller. The experimental results showed that in the experimental group with the intelligent drinking water controller, the average daily water waste per finishing pig was only 0.186 L (p < 0.05), accounting for only 25.98% of the average daily water waste per pig in the control group (p < 0.05). In terms of water quality, the intelligent drinking water controller also showed better performance, and the performance indicators were effectively reduced, with the highest reduction reaching 39.86%, which greatly reduced water pollution. Compared with the traditional drinking bowl, the average daily weight increment of fattening pigs in the pen using the intelligent drinking water controller was increased by 0.02 kg. In terms of long-term benefits, the PLC-based intelligent drinking water controller significantly improves the economic returns of the farm and has a positive impact on pig health. The high frequency data collection of the pigs’ drinking habits through the intelligent drinking water controller can also provide data support for the subsequent establishment of a pig water-drinking behavior analysis model. Full article