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Modern industrial systems routinely generate data in high volume at high velocity. These high-dimensional data streams (HDDS) provide valuable information at granular levels to quality personnel during root cause investigation in cases of a system fault. The goal of fault analysis using HDDS is twofold: (1) identify abnormal data streams and (2) locate the change point when the processes become out of control. Existing research has largely focused on addressing the two issues separately. In this article, we propose a unified framework by formulating the problem as optimal control of hierarchical missed discovery rates in multiple classifications. Theoretically, we establish that our approach minimizes the number of false discoveries while controlling the missed discovery rates at desired levels. Numerically, we develop a computationally efficient algorithm for solving the optimization and demonstrate its superior performance over the existing methods. A data-driven version of the proposed approach is suggested as well. An application to a real data set in semiconductor manufacturing shows that our approach works well in practice. [ABSTRACT FROM AUTHOR]

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regression, neural networks, decision trees, and gradient boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominance of the random forest model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings. [ABSTRACT FROM AUTHOR]

Bacterial biofilms pose significant challenges, from healthcare-associated infections to biofouling in industrial systems, resulting in significant health impacts and financial losses globally. Classic antimicrobial methods often fail to eradicate sessile microbial communities within biofilms, requiring innovative approaches. This review explores the structure, formation, and role of biofilms, highlighting the critical importance of exopolysaccharides in biofilm stability and resistance mechanisms. We emphasize the potential of microbial enzymatic approaches, particularly focusing on glycosidases, proteases, and deoxyribonucleases, which can disrupt biofilm matrices effectively. We also delve into the importance of enzymes such as cellobiose dehydrogenase, which disrupts biofilms by degrading polysaccharides. This enzyme is mainly sourced from Aspergillus niger and Sclerotium rolfsii, with optimized production strategies enhancing its efficacy. Additionally, we explore levan hydrolase, alginate lyase, α-amylase, protease, and lysostaphin as potent antibiofilm agents, discussing their microbial origins and production optimization strategies. These enzymes offer promising avenues for combating biofilm-related challenges in healthcare, environmental, and industrial settings. Ultimately, enzymatic strategies present environmentally friendly solutions with high potential for biofilm management and infection control. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors. [ABSTRACT FROM AUTHOR]

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm for protocol reverse analysis of EtherCAT. The algorithm combines sequence alignment and the Pearson correlation coefficient. We utilize value distribution statistics and the bit flip rate algorithm to effectively partition the protocol fields. We propose a semantics analysis method based on sequence alignment when HMI data and EtherCAT messages have a direct correlation. Conversely, for circumstances where there exists a decoding relationship between HMI data and EtherCAT messages, a semantic analysis method is proposed that employs the Pearson correlation coefficient. We completed a reverse analysis of the EtherCAT messages from an industrial robot system. By comparing the experiment results with the protocol description document, we validated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the deep implementation of the national "dual carbon" strategy, the development of a new power system dominated by renewable energy has accelerated significantly. Electrolytic aluminum load, as an important energy-intensive industrial resource, possesses response flexibility, providing a critical pathway for the efficient utilization of renewable energy. However, ensuring the safety of its production process during demand-side response remains a key challenge. This study systematically investigates the core production constraint of electrolytic aluminum load—electrolytic bath temperature—and its impacts on chemical reaction rates, current efficiency, and production equipment. A detailed coupling relationship between core production constraints and active power regulation is established. By quantifying the effects of temperature variation on the electrolytic aluminum production process, a demand-side response control cost model for electrolytic aluminum load is proposed. Additionally, a day-ahead scheduling model is developed with the objective of minimizing system operating costs while considering the participation of electrolytic aluminum load. Simulation results demonstrate that this method significantly reduces wind curtailment and load shedding while ensuring the safety of electrolytic aluminum production. It provides a novel approach for enhancing system economic efficiency, improving renewable energy utilization, and promoting the deep integration of power systems with industrial loads. [ABSTRACT FROM AUTHOR]

The fractional frequency offshore wind power system (FFOWPS) is a core technology for the long‐distance transmission and integration of the large‐scale offshore wind power. However, the risk of wide‐band oscillation in the FFOWPS has become increasingly prominent due to the inevitable presence of the power electronic devices. The traditional admittance of the frequency converter, which is a crucial component in the FFOWPS, may contain right‐half‐plane (RHP) poles when operating under various control modes. To address this deficiency, this paper establishes a four‐port small‐signal admittance model of a back‐to‐back frequency converter and demonstrates its interconvertibility with the traditional model. On this basis, the wide‐band oscillation analysis method for the FFOWPS is proposed, which explicitly describes the influence of the industrial frequency system, the fractional frequency system, and their coupling interactions on system stability. Simulation results in MATLAB/Simulink verify the proposed stability analysis method, highlight stability misjudgements associated with the traditional admittance, and analyse the impact of the coupling terms on system stability. [ABSTRACT FROM AUTHOR]

This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process. [ABSTRACT FROM AUTHOR]

The integration of blockchain and federated learning (FL) has emerged as a promising solution to address data privacy and security challenges in Intelligent Unmanned Ports (IUPs). However, existing blockchain federated learning (BFL) frameworks encounter significant limitations, including high latency, inefficient data processing, and limited scalability, particularly in scenarios with sparse and distributed data. This paper introduces a novel multi-chain federated learning (MFL) framework to overcome these challenges. The proposed MFL architecture interconnects multiple BFL chains to facilitate the secure and efficient aggregation of data across distributed devices. The framework enhances privacy and efficiency by transmitting aggregated model updates rather than raw data. A low-frequency consensus mechanism is employed to improve performance, leveraging game theory for representative selection to optimize model aggregation while reducing inter-chain communication overhead. The experimental results demonstrate that the MFL framework significantly outperforms traditional BFL in terms of accuracy, latency, and system efficiency, particularly under the conditions of high data sparsity and network latency. These findings highlight the potential of MFL to provide a scalable and secure solution for decentralized learning in IUP environments, with broader applicability to other distributed systems such as the Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

Concern has been raised about the potential greenwashing/co-optation of regenerative agriculture (RA) due to a lack of consensus on its definition. While the academic literature has cataloged various approaches to defining RA, each definitional approach carries with it a relative concern for its likelihood for co-optation and the potential transformative power it can have within the sector. As the industrial agrifoods sector is taking interest in the field, lessons from the organic movement are worth highlighting. The corporate system has easily integrated the foundational pillar of growing food without chemicals, but left behind the pillars of alternative food distribution, and a focus on whole foods and unprocessed ingredients. Corporate interest in RA could be a major driver for scaled adaptation, yet it may lose its focus on the regeneration of agriculture resources, ecosystems functions, and the social systems required to reproduce the next generation of farmers. The greatest challenge is that the fundamental concern is a philosophical one, which entails a shift in how humans perceive the natural world and their role in it. As RA scales, will it hold to its values and remain obscure, or could its values merge with the predominant industrial system to have significance and affect real change in agriculture? [ABSTRACT FROM AUTHOR]

Daucus carota L. extract is attracting interest as a natural colorant alternative. However, the presence of anthocyanins (ACNs), which are sensitive to pH changes, limits its application. To tackle this issue, water-in-oil-in-water (W1/O/W2) double emulsions are emerging as innovative solutions. Nevertheless, the problem of reaching robust colorant systems for industrial use still needs to be overcome. One important target is to reach a high coloring power, minimizing its impact on the final product. In this context, the effect of colorant concentration and the volume of the primary emulsion, two routes to increase the colorant power, on color attributes and stability, an important feature to reach a marketable product, was studied. The optimal experimental design was conducted to two optimal solutions, whether through heightened colorant concentration or primary emulsion volume: a 41/59 (W1/O)/W2 ratio with 11 wt.% colorant, and a 48/52 (W1/O)/W2 ratio with 6 wt.% colorant, respectively. A subsequent assessment of color and physical emulsion stability over 30 days pointed out the solution with the lower colorant concentration (6 wt.%) as the one with better performance (L*: 44.11 ± 0.03, a*: 25.79 ± 0.01, D4;3: 9.62 ± 0.1 µm, and CI: 14.55 ± 0.99%), also minimizing the permeability of the colorant to the outer aqueous phase. Overall, these optimized emulsions offer versatile coloring solutions suitable for various industrial applications, such as food matrices and functional cosmetics. [ABSTRACT FROM AUTHOR]

In recent decades, standardisation has become crucial in Industrial Automation Systems, promoting interoperability among devices from different manufacturers. Nonetheless, the portability of manufacturing engineering tools continues to present a significant challenge. AutomationML (IEC 62714 -5) has played a role in formulating strategies that leverage O-PAS Standard Function Blocks. The O-PAS specification includes several standards, such as IEC 62443, IEC 61499, IEC 61131–3, OPC-UA (Open platform communications unified architecture), DCS (Distributed control system), and PLC (Programmable logic controller). This highlights the necessity of integrating new technologies with legacy systems to achieve enhanced interoperability. In this context, this work proposes the development of User-Defined Function Blocks (UDFB) that adhere to O-PAS standards and can be implemented across any compliant system. The proposed methodology is aligned with O-PAS standards, leading to the inclusion of UDFB information in XML format within the Function Block Library and AutomationML. [ABSTRACT FROM AUTHOR]

In medium voltage and high-power drive applications, pulse width modulation (PWM) techniques are widely used to achieve effective speed control of AC motors. In real-time, an industrial drive system requires reduced hardware complexity and low computation time. The reliability of the AC drive can be improved with the FPGA (field programmable gate array) hardware equipped with digital controllers. To improve the performance of AC drives, a new FPGA-based Wavect real-time prototype controller (Xilinx ZYNQ-7000 SoC) is used to verify the effectiveness of the controller. These advanced controllers are capable of reducing computation time and enhancing the drive performance in realtime applications. The comparative performance analysis is carried out for the most commonly used voltage source inverter (VSI)-based PWM techniques such as sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) for three-phase, two-level inverters. The comparative study shows the SVPWM technique utilizes DC bus voltage more effectively and produces less harmonic distortion in terms of higher output voltage, flexible control of output frequency, and reduced harmonic distortion at output voltage for motor control applications. The simulation and hardware results are verified and validated by using MATLAB/Simulink software and FPGA-based Wavect real-time controller respectively. [ABSTRACT FROM AUTHOR]

The decarbonization of industrial energy systems which comprise different networks (such as steam, water, electric power, fuel sources) is crucial for mitigating climate change and achieving sustainability goals. This paper presents a comprehensive methodology integrated in an open-source in-house tool for the coupled design and operation optimization of energy systems in industrial settings. The proposed approach integrates advanced optimization techniques with modeling of energy systems including properties like mass flow and temperature to simultaneously optimize both design parameters and operational strategies. The methodology encompasses the optimized integration of various energy technologies, such as renewable energy technologies, energy storage, and power-to-heat technologies while considering changing operational conditions and variable energy demand and supply. A multi-objective optimization framework is employed to balance conflicting targets, such as minimizing greenhouse gas emissions, operational costs, and ensuring system reliability. The in-house tool application considering a case study based on a food industry process demonstrates the effectiveness of the proposed approach in significantly reducing carbon footprints as well as operational and investment costs compared to traditional low-fidelity methods incorporated in commercial tools. The optimized concept achieved through the in-house tool has shown 8.5% less emission (EMI) compared to the optimized designs of the commercial tool. It shows 36% reduction in CO2 emissions compared to the existing facility of the case study. The optimized energy concept can be implemented in the existing facility with a payback period of 4.6 years. The outcomes of the selected use-case highlight the importance of coordinated design and operation decisions in achieving optimal performance and sustainability in industrial energy systems. It also shows an ideal workflow for making optimized design decisions to decarbonize industry with novel energy concepts. Thus, this work provides a robust foundation for future research and practical applications aimed at accelerating the transition towards low-carbon industrial processes. [ABSTRACT FROM AUTHOR]

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry. [ABSTRACT FROM AUTHOR]

Autonomous Guided Vehicles (AGVs) play an important role in the automation of material handling and transportation tasks in modern industrial and logistics systems. However, suboptimal path planning and longer waiting times at charging stations significantly affect the operational efficiency of these vehicles. To address these challenges, we leverage the capabilities of Graph Neural Networks (GNN) to find the optimal paths for AGVs. In this paper, we propose GRouteNet, a GNN-based model that effectively finds the shortest path for AGVs. The proposed model utilizes the message-passing mechanism of GNN to determine the neighbor nodes and then aggregates this information to find the shortest path. We compare the results of GRouteNet with a couple of existing state-of-the-art pathfinding models and show that the path length computed by GRouteNet is up to 45% shorter compared to the existing models. Furthermore, we propose a Shortest Charging Time First (SCTF) scheduling algorithm to reduce the long waiting times in the queues at the charging stations. The proposed SCTF algorithm prioritizes the charging of AGVs based on their charging time and charges the AGVs with the shortest charging time first. We compare the results of SCTF with the first-come-first-serve scheduling algorithm and show that SCTF reduces the waiting times at the charging stations by up to 42%. [ABSTRACT FROM AUTHOR]

This review article indicates the untapped edible termites' nutritional potential and viability as a sustainable resource for future industrial applications. The global quest for sustainable and nutrient-rich food sources has intensified recently. Edible termites are a promising solution to address these challenges. Our study delves into the nutritional composition of edible termites, highlighting their rich protein content, essential amino acids, and valuable micronutrients. We assess their potential as a sustainable alternative for addressing malnutrition and meeting the dietary needs of a growing global population. Regarding industrial applications, we scrutinize the feasibility of incorporating edible termites into food production, their economic viability, scalability, and technological aspects of integrating their products into existing industrial systems. The findings indicate that in the midst of global food insecurity, incorporating edible termites into our industrial practices emerges as a promising avenue for fostering sustainability and resilience in the face of evolving environmental and nutritional challenges. [ABSTRACT FROM AUTHOR]

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model. • The symmetric dot pattern (SDP) is used to convert a one-dimensional signal into a two-dimensional visual image. • A multi-sensor fusion (MSF) method based on multi-channel SDP images is proposed. • A multi-channel capsule network based on self-attention mechanism is developed. • The proposed method is verified by fusion diagnosis of multi-channel isomorphic spatial information and heterogeneous information. [ABSTRACT FROM AUTHOR]

Static random-access memory (SRAM) is in great demand due to the development of portable electronics and its growing popularity in system-on-chip and advanced very-large-scale-integration (VLSI) of integrated circuits. Given the fact that memory takes up to 70% of total chip area, SRAM cell optimization has become a focus of research. In this work, a six-transistor SRAM (6T-SRAM) cell is designed using core insulator double-gate (CIDG) MOSFETs due to their reduced short-channel effects (SCEs). We showed in our previous work that the CIDG MOSFETs provide better digital performance than DG MOSFETs and are negligibly affected by quantum effects. In nanoscale MOSFET technologies, the continual scaling of the supply voltage (VDD) and dimensions of transistors adversely affects the stability of SRAM cells, i.e., read noise margin (RNM) and write noise margin (WNM). The 6T-SRAM cell designed using CIDG MOSFETs improves the WNM by 3.478% and 2.1837% for cell ratio (CR) and pull-up ratio (PR) equal to 0.7, respectively, as compared to a conventional DG 6T-SRAM cell. Further, its RNM and WNM have been found to be superior as compared to a DG MOSFET SRAM by 6.19% and 3.99%, respectively, for equally sized transistors, i.e., CR and PR equal to 1. The CIDG SRAM cell occupies approximately 4 times lower layout area as compared to TSMC's 20-nm high-density SRAM cell. The findings of present study confirm that CIDG SRAM cells can be used in applications requiring higher stability and density. The proposed SRAM utilizing a CIDG can be integrated into a radio-frequency (RF) transceiver circuit for an industrial wireless system. [ABSTRACT FROM AUTHOR]

Since its origin in 15th century European imperialism, the globe has been an object of conquest involving regimes of territorial exclusion and various forms of land abstraction now known as nationalism, colonialism, capitalism, and industrialism. Coming to think like the Earth system and generating politics grounded in it could pose a welcome disruption of these systematically controlling orders only if such planetary thinking is grounded in a nondominating orientation. We propose that this grounding be geological wonder, the open consideration of Earth as a system exceeding human narcissism. Following Dipesh Chakrabarty's distinction between the globe and the planet, we articulate planetarism in an emerging cosmology decentering human life while emphasizing human limits. Globality, by contrast, is a modern, narcissistic formation that geological wonder unsettles. We draw on Martha C. Nussbaum's politics of wonder to articulate a postglobal, disruptive virtue. [ABSTRACT FROM AUTHOR]

In today's expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model's real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments. [ABSTRACT FROM AUTHOR]

Integration of the Internet of Things (IoT) in industrial settings necessitates robust cybersecurity measures to mitigate risks such as data leakage, vulnerability exploitation, and compromised information flows. Recent cyberattacks on critical industrial systems have highlighted the lack of threat analysis in software development processes. While existing threat modeling frameworks such as STRIDE enumerate potential security threats, they often lack detailed mapping of the sequences of threats that adversaries might exploit to apply cyberattacks. Our study proposes an enhanced approach to systematic threat modeling and data flow-based attack scenario analysis for integrating cybersecurity measures early in the development lifecycle. We enhance the STRIDE framework by extending it to include attack scenarios as sequences of threats exploited by adversaries. This extension allows us to illustrate various attack scenarios and demonstrate how these insights can aid system designers in strengthening their defenses. Our methodology prioritizes vulnerabilities based on their recurrence across various attack scenarios, offering actionable insights for enhancing system security. A case study in the automotive industry illustrates the practical application of our proposed methodology, demonstrating significant improvements in system security through proactive threat modeling and analysis of attack impacts. The results of our study provide actionable insights to improve system design and mitigate vulnerabilities. [ABSTRACT FROM AUTHOR]

The Controller Area Network (CAN) has been adopted in various reliability-critical industrial systems. However, intermittent connection (IC) problems of network cables may worsen system performance and even threaten operational safety. Recently, there have been several studies on diagnosing intermittent open circuit faults, but the intermittent short circuit (ISC) fault diagnosis has not been addressed. In this paper, a novel ISC fault location method for CANs is proposed based on two-port network modeling. First, the CAN network is modeled as a switched system that depends on the states of the sending nodes using a two-port network approach. An equivalent circuit model and a voltage transfer difference function (VTDF) group are derived for each state where one particular node is sending. Second, upon each fault, corresponding direction events are defined by comparing the two VTDF values that are calculated from the voltages collected at network ends. Then, the fault and health domains can be determined by integrating these direction events with the network topology information according to their statistical significance. Third, a bidirectional eviction localization algorithm is developed to identify ISC fault locations based on the fault and health domains. A testbed is constructed, and case studies are conducted to demonstrate that the proposed method can correctly locate the ISC faults in various network topological layouts. [ABSTRACT FROM AUTHOR]

In modern manufacturing, there is an increasing demand for reliable in-process measurement methods directly on large CNC machine tools, eliminating the need to transport workpieces to metrological laboratories. This study assesses the capability and applicability of an articulated arm coordinate measuring machine and a machine tool touch-trigger probe when measuring to a specified tolerance of 0.05 mm in a production environment. Experiments were conducted using the KOBA calibration standard and included measurements with and without applying the articulated arm coordinate measuring machine leapfrog method. The results were evaluated according to ISO 22514-7:2021 and ISO 14253-1:2017, which establish criteria for measurement system capability. The findings revealed that neither measurement system met the capability requirements of ISO 22514-7:2021, particularly due to unsatisfactory QMS and CMS values. However, under ISO 14253-1:2017, both systems were deemed conditionally suitable for verifying conformity to the specifications, with the articulated arm coordinate measuring machine showing lower applicability when using the leapfrog method. This research supports the idea that unreasonable demands for compliance with current standards may lead to questioning of the systems that previously met older standards. The study contributes to the ongoing discussion on integrating advanced metrological tools into the manufacturing process and underscores the need for careful evaluation to ensure the capability and reliability of measurement systems in industrial practice. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence(AI) technologies into identity and access management (IAM) systems has greatly improved access control and management, offering more robust, adaptive, and intelligent solutions than traditional methods. AI-driven IAM systems enhance security, operational efficiency, and introduce new capabilities in industrial environments. In this narrative review, we present the state-of-the-art AI technologies in industrial IAM, focusing on methods such as biometric, comprising facial and voice recognition, and multifactor authentication for robust security. It addresses the challenges and solutions in implementing AI-based IAM systems in industrial settings, including security, privacy, evaluation, and continuous improvement. We present also the emerging trends and future directions, highlighting AI's potential to transform industrial security measures. This review aims to guide researchers and practitioners in developing and implementing next-generation access control systems, proposing future research directions to address challenges and optimize AI applications in this domain. [ABSTRACT FROM AUTHOR]

The predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults. [ABSTRACT FROM AUTHOR]

Micro-textured surfaces show promise in improving tribological properties, but predicting their performance remains challenging due to complex relationships between surface features and frictional behavior. This study evaluates five algorithms—linear regression, decision tree, gradient boosting, support vector machine, and neural network—for their ability to predict load-carrying capacity and friction force based on texture parameters including depth, side length, surface ratio, and shape. The neural network model demonstrated superior performance, achieving the lowest MAE (24.01) and highest R-squared value (0.99) for friction force prediction. The results highlight the potential of machine learning techniques to enhance the understanding and prediction of friction-reducing micro-textures, contributing to the development of more efficient and durable tribological systems in industrial applications. [ABSTRACT FROM AUTHOR]

Today's customer lifestyles have reshaped their expectations and preferences, driving a growing demand for tailor-made products. While current conventional manufacturing (MNF) systems are robust, they often lack the flexibility needed to accommodate customization. Most MNF systems, despite advances in technology and machinery, still rely on executing predefined instructions, limiting their flexibility. In contrast, human workers excel at handling product variations due to their cognitive abilities, which allow them to perceive, analyze, and make appropriate decisions to adapt to changing conditions. This study introduces virtual numerical control (VNC) as a solution to upgrade MNF systems and overcome these limitations. VNC aims to transform MNF systems into cognitive entities capable of autonomous decision-making, enabling greater flexibility to meet customization demands. To demonstrate the potential of VNC, we implemented it in a welding system as a practical case study. The results showed that VNC enabled the system to operate autonomously. It accurately identified the shape of the objects to be welded, determined the appropriate welding paths, and executed them with high precision, all without human intervention. This highlights the significant potential of VNC technology for broader applications in industrial automation in welding and beyond. [ABSTRACT FROM AUTHOR]

In the context of building a modern industrial system, digital finance inclusive (DFI) and environmental regulation (ER) have become significant drivers of industrial structure upgrading. Amid the rapid development of DFI and the tightening of ER, high-quality development goals have been set. From a spatial perspective, can DFI, ER, and their interactions positively impact China's industrial structure? Based on panel data from 30 provinces in China from 2011 to 2020, empirical analysis is conducted using the Spatial Durbin Model and the Threshold Model. The study find that, firstly, in spatial regression, both DFI and ER significantly promote the upgrading of industrial structures, with notable spatial spillover effects. Secondly, it was discovered that the interaction between DFI and ER have a significant negative impact on industrial structure upgrading, with a pronounced negative spatial spillover effect, indicating a "siphon effect," which is a new finding. In the threshold model regression, it is found that both DFI and ER exhibit a single-threshold effect, while their interaction shows a double-threshold effect. In the heterogeneity test, it is found that the interaction between DFI and ER on industrial structure upgrading is greatly influenced by regional factors, with significant differences across different regions. Therefore, it is crucial to pay attention to the threshold values in the threshold effect while considering the unique economic and geographical attributes of each region to formulate targeted strategies. [ABSTRACT FROM AUTHOR]

Low-density foreign objects (LDFOs) in foods pose significant safety risks to consumers. Existing detection methods, such as metal and X-ray detectors, have limitations in identifying low-density and nonmetallic contaminants. To address these challenges, our research group constructed and optimized a continuous-wave sub-terahertz (THz) imaging system for the real-time, on-site detection of LDFOs in infant snacks. The system was optimized by adjusting the attenuation value from 0 to 9 dB and image processing parameters [White (W), Black (B), and Gamma (G)] from 0 to 100. Its detectability was evaluated across eight LDFOs underneath snacks with scanning at 30 cm/s. The optimal settings for puffed snacks and freeze-dried chips were found to be 3 dB attenuation with W, B, and G values of 100, 50, and 80, respectively, while others required 0 dB attenuation with W, B, and G set to 100, 0, and 100, respectively. Additionally, the moisture content of infant snacks was measured using a modified AOAC-based drying method at 105 °C, ensuring the removal of all free moisture. Using these optimized settings, the system successfully detected a housefly and a cockroach underneath puffed snacks and freeze-dried chips. It also detected LDFOs as small as 3 mm in size in a single layer of snacks, including polyurethane, polyvinyl chloride, ethylene–propylene–diene–monomer, and silicone, while in two layers of infant snacks, they were detected up to 7.5 mm. The constructed system can rapidly and effectively detect LDFOs in foods, offering a promising approach to enhance safety in the food industry. [ABSTRACT FROM AUTHOR]

To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost. [ABSTRACT FROM AUTHOR]

Bioactive food ingredients contribute to the promotion and maintenance of human health and wellbeing. However, these functional ingredients often exhibit low biopotency after food processing or gastrointestinal transit. Well‐designed oral delivery systems can increase the ability of bioactive food ingredients to resist harsh environments inside and outside the human body, as well as allow for controlled or triggered release of bioactives to specific sites in the gastrointestinal tract or other tissues and organs. This review presents the characteristics of common bioactive food ingredients and then highlights the barriers to their biopotency. It also discusses various oral delivery strategies and carrier types that can be used to overcome these biopotency barriers, with a focus on recent advances in the field. Additionally, the advantages and disadvantages of different delivery strategies are highlighted. Finally, the current challenges facing the development of food‐grade oral delivery systems are addressed, and areas where future research can lead to new advances and industrial applications of these systems are proposed. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain. [ABSTRACT FROM AUTHOR]

Multistatic imaging systems are commonly used in radar systems and microwave imaging. In these systems, many antennas are arranged three-dimensionally and connected to RF switches. The length of each transmitter (Tx) and receiver (Rx) channel differs slightly, causing artifacts in high-resolution image reconstruction. This study presents a novel method for the phase calibration of multistatic systems. This method does not require system reconstruction and can automatically perform phase calibration in a short time. This method is expected to facilitate an accurate phase measurement in multistatic systems. The approach involves phase calibration by analyzing the reflection coefficients of antenna elements in the time domain. Imaging experiments were performed on a multistatic imaging system using this calibration method, and the position and shape of a metal rod with a diameter one-fourth of a wavelength were reconstructed by simple back-projection with an accuracy beyond the diffraction limit. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and efficiency of industrial systems. This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. The model showed better performance than traditional approaches, with an RMSE of 0.989. (2) A Deep Neural Network (DNN) was designed to predict the extended RUL of bearings after laser shock peening (LSP) remanufacturing. The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Industry 5.0 paradigm puts people and their applications at the center of attention and, with the increasing automation and robotization of work, the need for workers to acquire new, more advanced skills increases. The development of artificial intelligence (AI) means that expectations for workers are further raised. This leads to the need for multiple career changes from life and throughout life. Belonging to a previous generation of workers makes this retraining even more difficult. The authors propose the use of machine learning (ML), virtual reality (VR) and brain–computer interface (BCI) to assess the conditions of work–life balance for employees. They use machine learning for prediction, identifying users based on their subjective experience of work–life balance. This tool supports intelligent systems in optimizing comfort and quality of work. The potential effects could lead to the development of commercial industrial systems that could prevent work–life imbalance in smart factories for Industry 5.0, bringing direct economic benefits and, as a preventive medicine system, indirectly improving access to healthcare for those most in need, while improving quality of life. The novelty is the use of a hybrid solution combining traditional tests with automated tests using VR and BCI. This is a significant contribution to the health-promoting technologies of Industry 5.0. [ABSTRACT FROM AUTHOR]

In this study, a stepper driver, which is suitable for use in the automotive industry, designed for general use, capable of adaptive learning in the systems in which it is used, and with CAN and universal asynchronous receiver–transmitter (UART) communication options, was designed. This design was made with a PIC18F25K22 microcontroller unit (MCU). Rotor speed, armature current, and terminal voltage can be measured on the proposed brushed DC motor drive system. It gives the desired response by evaluating obstacles and other situations in which high currents are drawn from the source. The speed measurement of the vehicle and the open and closed status of an automatic door can be monitored. The main contribution of the designed PCB is an adaptive learning algorithm (ALA) that uses the pulses of the encoder, estimates the position of the step, and manages the operation process to prevent mechanical damage at the final point of the motor. Furthermore, unexpected hitting and pinching which are defined as obstacles to the driver can be controlled by monitoring the current value drawn from the driver. The benefit of this method is that the life of the mechanical step is increased due to the management of the forward and backward step operation, preventing potential accidents. [ABSTRACT FROM AUTHOR]

Introduction: China's manufacturing sector is a cornerstone of its industrial system. However, some parts of China are dominated by heavy chemicals, which leads to severe environmental pollution. The emergence of green finance (GF) can influence social capital to gradually withdraw from companies that emit pollution and increase investment in environmentally friendly ones. Methods: This paper selects the data related to GF and the manufacturing industry (MI) from 2011 to 2020 by exploring the effect of GF on the transformation and upgrading of manufacturing industries (MIU) through literature analysis and the Spatial Durbin model. Results: The results demonstrate that GF positively influences the promotion of MIU, whereas MIU has a good spatial spillover effect. The results of this regression remain robust after a series of tests. Discussion: This research suggests that the government and enterprises should pay attention to GF and encourage more enterprises to take the initiative to adjust the industrial direction and achieve a clean upgrade in manufacturing. [ABSTRACT FROM AUTHOR]

The glass, ceramic and cement plants are energy intensive industrial systems, releasing high amounts of greenhouse gases. The dissipation of the waste heat reduces their profitability, energy and ecological efficiency. The absorption refrigeration systems are successful solutions for the recovery of thermal energy at different temperature levels to obtain useful cooling and heating powers for technological needs, and for air conditioning of administrative and industrial buildings. Although the absorption units in a heat pump and refrigeration cycle are currently used in building and industrial systems, they have not yet penetrated widely into the silicate factories. This paper discusses possibilities for the applications of basic types of absorption cycles at heat pump and refrigeration modes to utilize waste energy at different temperature levels in glass, ceramic and cement plants. The examined variants are demonstrated via examples, diagrams and analyses of the possible energy saving. [ABSTRACT FROM AUTHOR]

In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system's ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems. [ABSTRACT FROM AUTHOR]

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality. [ABSTRACT FROM AUTHOR]

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications. [ABSTRACT FROM AUTHOR]

Green energy transformation requires comprehensive strategies that include both innovations in energy production and more efficient energy use. This article investigates the potential for saving electrical energy in industrial automation systems by utilizing bistable switching/relay. Compared to traditional systems, these innovative solutions demonstrate significant reductions in energy consumption. A market analysis of available bistable relays, along with experimental determination of their control conditions, highlights their application potential and indicates the benefits of their implementation. The findings suggest that replacing classical relays with their bistable counterparts could significantly contribute to global sustainability efforts. The article presents the process of redesigning a standard industrial relay into a bistable design. Adding two additional elements achieved the intended bistable functionality. The article calls for increased research and investment in such technologies, emphasizing that the energy-saving potential offered by bistable switching/relay circuits should not be overlooked. [ABSTRACT FROM AUTHOR]

Neural network (NN)-based methods are extensively used for intelligent fault diagnosis in industrial systems. Nevertheless, due to the limited availability of faulty samples and the presence of noise interference, most existing NN-based methods perform limited diagnosis performance. In response to these challenges, a self-adaptive selection graph pooling method is proposed. Firstly, graph encoders with sharing parameters are designed to extract local structure-feature information (SFI) of multiple sensor-wise sub-graphs. Then, the temporal continuity of the SFI is maintained through time-by-time concatenation, resulting in a global sensor graph and reducing the dependency on data volume from the perspective of adding prior knowledge. Subsequently, leveraging a self-adaptive node selection mechanism, the noise interference of redundant and noisy sensor-wise nodes in the graph is alleviated, allowing the networks to concentrate on the fault-attention nodes. Finally, the local max pooling and global mean pooling of the node-selection graph are incorporated in the readout module to get the multi-scale graph features, which serve as input to a multi-layer perceptron for fault diagnosis. Two experimental studies involving different mechanical and electrical systems demonstrate that the proposed method not only achieves superior diagnosis performance with limited data, but also maintains strong anti-interference ability in noisy environments. Additionally, it exhibits good interpretability through the proposed self-adaptive node selection mechanism and visualization methods. • Introducing SASGPool: A novel data-driven fault diagnosis method. • SASGPool excels with limited samples and noise interference. • Diverse graph topologies capture multiscale sensor data relationships. • Innovative node selection sharpens focus on fault-attention features. • SASGPool achieves precise fault diagnosis in electrical and mechanical systems. [ABSTRACT FROM AUTHOR]

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Cannabis sativa, a versatile plant containing over 150 cannabinoids, is increasingly valued for its medicinal properties. It is classified into hemp and marijuana based on its Δ9-tetrahydrocannabinol (Δ9-THC) content. The objective of this study was to optimize cannabinoid production in hemp within a vertical farming system by investigating the effects of methyl jasmonate (MeJA) on plant growth and specific cannabinoid contents. After propagating hemp plants, they were treated with various concentrations of MeJA (0, 100, 200, and 400 μM). Plant growth parameters, glandular trichome (GT) density, and the contents of specific cannabinoids—cannabidiolic acid (CBDA), cannabidiol (CBD), tetrahydrocannabinolic acid (THCA), and Δ9-THC—were analyzed. The results showed that MeJA treatment decreased plant height and leaf area while increasing GT density and the synthesis of CBDA and THCA at lower concentrations. Specifically, treatment with 100 μM MeJA provided optimal conditions for enhancing cannabinoid production while controlling plant height, which is advantageous for vertical farming. These findings suggest that precise application of MeJA in controlled environments can increase yields of valuable cannabinoids with efficient use of space, thereby enhancing the commercial and medicinal value of hemp. [ABSTRACT FROM AUTHOR]

The transition to electric vehicles (EVs) has introduced new challenges in lubrication, demanding innovative solutions to ensure consistent performance. One promising approach is the use of nanoparticle additives, which have the potential to improve lubrication performance significantly. However, achieving a stable suspension of these nanoparticles in lubricating oils remains a critical challenge, as suspension stability is essential for maintaining consistent performance and maximizing the benefits of these advanced additives. In this study, carbon nanoparticles (CNPs) were modified with dodecylamine (DDA) to achieve stable suspension in nonpolar fluids. The successful functionalization was confirmed by the FTIR results, which showed characteristic peaks of various bonding. The suspension stability tests demonstrated that DDA-CNPs remained suspended for over 60 days in the Polyalphaolefin (PAO) oil, whereas unmodified CNPs were sedimented within 3–7 days. The rheological behavior was measured under different shear rates and temperatures. Viscosity measurements indicated that DDA-CNPs maintained a lower value compared to base PAO. The lubricants' friction coefficient (COF) was also determined under various speeds and loads. The addition of DDA-CNPs at a concentration of 0.05 wt.% resulted in a significant reduction in COF, with values decreasing by 26% compared to base PAO oil under a load of 1 N. Additionally, the COF for DDA-CNPs was consistently lower than that of PAO, with reductions ranging from 15% to 18% across all tested speeds. The Stribeck curve further highlighted the improved performance of DDA-CNPs across boundary, mixed, and hydrodynamic lubrication regimes. These findings suggest that DDA-CNPs significantly improve the lubrication performance of PAO oil, making them suitable for advanced lubrication applications in automotive and industrial systems. [ABSTRACT FROM AUTHOR]

Through system identification for robust control methods and utilizing real-time experimental field data, a comprehensive mathematical model is derived that represents the dynamic performance of a single electrode positioning system (EPS) in an industrial electric arc melting furnace (EAF). This EPS is characterized by large, time-varying dynamic parameters, which fluctuate based on operating conditions, specifically as the electrode weight changes within its operational range. The system identification methodology for robust control is developed in four main steps, progressing from experimental design to model validation. This approach yields a nominal model of the actual system and provides a trustworthy estimate of the region of uncertainty of the model, bounded by models of the real system under maximum and minimum electrode weight conditions (limit operating models). The methodology generates three fourth-order time-delay models using an ARMAX structure. The results are promising, as system identification for robust control enables the derivation of mathematical models specifically tailored for designing robust controllers. These controllers significantly enhance the EPS control system's performance and substantially reduce energy consumption and environmental emissions. [ABSTRACT FROM AUTHOR]