Industrial Protocols Research Articles

A substrate-induced dual utilization cascade reaction of N-alkyl anilines: highly site-selective sustainable synthesis of chromeno[4,3-b]quinolin-6-ones.

We describe a novel substrate-induced cascade reaction of N-alkyl anilines via C(sp3)-N bond cleavage and reorganization into chromeno[4,3-b]quinolin-6-ones. N-alkyl anilines are not only used as a carbon source, but also the waste group was trapped by 4-hydroxy coumarin. The calculation of green metrics shows that our protocol is more efficient and green with lower emission than before. Additionally, a gram-scale reaction and one-pot synthesis of bioactive molecules further demonstrate the potential application of our protocol in the pharmaceutical industry.

Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning

Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.

Shadow Testing in Autonomous Vehicles : A Novel Approach to Validating Full Self-Driving AI Systems

As autonomous vehicles progress towards widespread deployment, ensuring the reliability and safety of Full Self-Driving (FSD) AI systems remains a critical challenge. This article presents a comprehensive analysis of shadow testing, an innovative approach to validating new FSD AI models in real-world conditions without compromising user safety. We examine the methodology of deploying new AI models in "shadow mode," where they process live sensory data alongside the operational system but do not control the vehicle. This approach enables the collection of valuable performance metrics and the identification of edge cases while mitigating risks associated with direct deployment. Our study demonstrates how shadow testing facilitates a continuous feedback loop for iterative improvement, enhancing model reliability through data-driven refinement. We further explore the challenges of implementing shadow testing at scale, including data processing requirements and ethical considerations. Our findings suggest that shadow testing not only accelerates the development of robust FSD systems but also plays a crucial role in building public trust in autonomous vehicle technology. This article contributes to the growing body of knowledge on AI safety validation techniques and provides insights for standardizing testing protocols in the autonomous vehicle industry.

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

Investigation of Salmonella enteritidis Growth under Varying Temperature Conditions in Liquid Whole Egg: Proposals for Smart Management Technology for Safe Refrigerated Storage

This study investigates the growth characteristics of Salmonella enteritidis (S. enteritidis) in liquid whole egg under both isothermal and non-isothermal storage conditions to understand the risks associated with inadequate temperature management in the egg industry. Using controlled laboratory simulations, liquid whole egg samples inoculated with S. enteritidis were stored under various isothermal (5, 15, 25, 35, and 45 °C) and non-isothermal conditions (5–10, 15–20, 25–30, 35–40, and 45–50 °C). The growth behavior of the S. enteritidis was analyzed using a two-step predictive modeling approach. First, growth kinetic parameters were estimated using a primary model, and then the effects of temperature on the estimated specific growth rate and lag time were described using a secondary model. Independent growth data under both isothermal and non-isothermal conditions were used to evaluate the models. The results showed that S. enteritidis exhibits different growth characteristics depending on temperature conditions, emphasizing the need for strict temperature control to prevent foodborne illnesses. To address this, a predictive growth model tailored for non-isothermal conditions was developed and validated using experimental data, demonstrating its reliability in predicting S. enteritidis behavior under dynamic temperature scenarios. Additionally, temperature management technologies were proposed and tested to improve food safety during refrigerated storage. This study provides a scientific basis for improving food safety protocols in the egg industry, thereby protecting public health and maintaining consumer confidence amid temperature fluctuations.

Mechanistic insights into how mixing factors govern polyelectrolyte-surfactant complexation in RNA lipid nanoparticle formulation

Hypothesis: Lipid nanoparticle self-assembly is a complex process that relies on ion pairing between nucleic acids and hydrophobic cationic lipid counterions for encapsulation. The chemical factors influencing this process, such as formulation composition, have been the focus of recent research. However, the physical factors, particularly the mixing protocol, which directly modulates these chemical factors, have yet to be mechanistically examined using a reproducible mixing platform comparable to the industry standard. We here utilize Flash NanoPrecipitation (FNP), a scalable rapid mixing platform, to isolate and systematically investigate how mixing factors influence this complexation step, first by using a model polyelectrolyte-surfactant system and then generalizing to a typical RNA lipid nanoparticle formulation.Experiments: Aqueous polystyrene sulfonate (PSS) and cetrimonium bromide (CTAB) solutions are rapidly homogenized using reproducible FNP mixing and controlled flow rates at different stoichiometric ratios and total solids concentrations to form polyelectrolyte-surfactant complexes (PESCs). Then, key mixing factors such as total flow rate, inlet stream relative volumetric flow rate, and magnitude of flow fluctuation are studied using both this PESC system and an RNA lipid nanoparticle formulation.Findings: Fluctuations in flow as low as ± 5 % of the total flow rate are found to severely compromise PESC formation. This result is replicated in the RNA lipid nanoparticle system, which exhibited significant differences in size (132.7 nm vs. 75.6 nm) and RNA encapsulation efficiency (34.0 % vs. 82.8 %) under fluctuating vs. steady flow. We explain these results in light of the chemical variables isolated and studied; slow or nonuniform mixing generates localized concentration gradients that disrupt the balance between the hydrophobic and electrostatic forces that drive complex formation. These experiments contribute to our understanding of the complexation stage of lipid nanoparticle formation and provide practical insights into the importance of developing controlled mixing protocols in industry.

Review of intrusion detection system in cyber‐physical system based networks: Characteristics, industrial protocols, attacks, data sets and challenges

AbstractCyber‐Physical Systems (CPSs) provide critical infrastructure for the betterment of human lives thereby integrating cyber and physical components but the fusion of physical and digital components leads to an increase in the attack surface, which in turn provides opportunities for the attackers to intrude on these systems, which can affect the critical services like health care, water treatment facility, the electrical grid, hydropower plant, and so forth. The existing intrusion detection systems (IDSs) in CPSs are facing issues like poor detection accuracy, high false alarm rate and more computation time. Also, existing intrusion detection systems cannot identify new attacks that is, zero‐day assaults. Prerequisite exists for the design of the framework for detecting intrusions using artificial intelligence inspired approaches grounded on the principle of ensemble techniques. Intrusion detection framework assimilating ensemble‐based technique for CPS has been proposed. Since the proposed framework incorporates privileges of multiple techniques for intrusion detection and classification, hence the proposed framework may overcome the limitations of existing IDSs for CPS. Overview of cutting‐edge incursion identification methods for CPSs is presented. Several characteristics of CPS, industrial protocols, and anomaly detection techniques for intrusion detection are analyzed. Taxonomy of IDS for CPS has been proposed, taxonomy of attacks and threats on CPS has been intended. Research challenges for IDS in CPS are also uncovered from this review.

Integration of Legacy Industrial Equipment in a Building-Management System Industry 5.0 Scenario

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost.

Design and Development of a Flexible Manufacturing Cell Controller Using an Open-Source Communication Protocol for Interoperability

Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of consideration in the development of such a system. This study aims to describe the approach, from concept to implementation, for the development of the controller for a flexible manufacturing cell consisting of heterogeneous equipment in terms of functions and communication interfaces. Emphasis is put on the considerations and challenges for effective integration, extensibility, and interoperability. Scheduling and monitoring performed by the developed controller are demonstrated for a manufacturing cell producing microfluidic devices (bioMEMS) that consists of six workstations and a robot-based handling system. Communication between the system controller and the workstations was based on open-source technologies instead of proprietary software and protocols, to support interoperability and, to a considerable extent, code reusability.

State of research: Relevance of Computer Emergency Response Teams in Operational Technology

The increasing integration of Information Technology (IT) and Operational Technology (OT) in industrial environments has led to increased vulnerability to cyber threats. This article examines the need for a dedicated Computer Emergency Response Team (CERT) for OT to ensure the security, integrity, and resilience of critical infrastructure, particularly in the energy sector. OT is subject to specific challenges that differ from those in traditional IT networks. Cyberattacks on OT systems can not only cause financial losses, but also have a significant impact on physical security and the environment. A specific CERT for OT is necessary to address the unique characteristics of these environments. This requires expertise in industrial protocols, control systems and SCADA systems. The CERT for OT should be able to respond quickly to security incidents, perform forensic analysis and implement effective countermeasures to ensure business continuity. Research shows that implementing a specialized CERT for OT leads to improved threat detection, faster response, and more effective defense against attacks. In addition, this article emphasizes the importance of collaboration and communication between IT and OT security teams to ensure comprehensive system resilience. The following article provides a detailed literature analysis that comprehensively examines the current state of research on CERTs in the context of OT. The analysis of the relevant literature highlights the increasing threat to OT systems and emphasizes the specific requirements arising from the integration of IT and OT. By identifying research gaps and summarizing current findings, this article provides a comprehensive overview of the existing literature on this topic.

Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.

Overcoming Electrostatic Interaction via Pulsed Electroreduction for Boosting the Electrocatalytic Urea Synthesis

AbstractElectrocatalytic urea synthesis under ambient conditions offers a promising alternative strategy to the traditional energy‐intensive urea industry protocol. Limited by the electrostatic interaction, the reduction reaction of anions at the cathode in the electrocatalytic system is not easily achievable. Here, we propose a novel strategy to overcome electrostatic interaction via pulsed electroreduction. We found that the reconstruction‐resistant CuSiOx nanotube, with abundant atomic Cu−O−Si interfacial sites, exhibits ultrastability in the electrosynthesis of urea from nitrate and CO2. Under a pulsed potential approach with optimal operating conditions, the Cu−O−Si interfaces achieve a superior urea production rate (1606.1 μg h−1 mgcat.−1) with high selectivity (79.01 %) and stability (the Faradaic efficiency is retained at 80 % even after 80 h of testing), outperforming most reported electrocatalytic synthesis urea catalysts. We believe our strategy will incite further investigation into pulsed electroreduction increasing substrate transport, which may guide the design of ambient urea electrosynthesis and other energy conversion systems.

Overcoming Electrostatic Interaction via Pulsed Electroreduction for Boosting the Electrocatalytic Urea Synthesis.

Electrocatalytic urea synthesis under ambient conditions offers a promising alternative strategy to the traditional energy-intensive urea industry protocol. Limited by the electrostatic interaction, the reduction reaction of anions at the cathode in the electrocatalytic system is not easily achievable. Here, we propose a novel strategy to overcome electrostatic interaction via pulsed electroreduction. We found that the reconstruction-resistant CuSiOx nanotube, with abundant atomic Cu-O-Si interfacial sites, exhibits ultrastability in the electrosynthesis of urea from nitrate and CO2. Under a pulsed potential approach with optimal operating conditions, the Cu-O-Si interfaces achieve a superior urea production rate (1606.1 μg h-1 mgcat. -1) with high selectivity (79.01 %) and stability (the Faradaic efficiency is retained at 80 % even after 80 h of testing), outperforming most reported electrocatalytic synthesis urea catalysts. We believe our strategy will incite further investigation into pulsed electroreduction increasing substrate transport, which may guide the design of ambient urea electrosynthesis and other energy conversion systems.

Security and Privacy Column

Cryptographic protocols are really hard to design: they mix difficulties stemming from distributed systems, rely on cryptographic primitives and need to provide complex guarantees in the presence of an arbitrary attacker running in parallel. Rigorous security proofs are basically the only hope to avoid design flaws as witnessed by numerous examples. This has been recognized since the early 80's and two main approaches have been actively developed since. The computational model is rooted in complexity theory and relies on a reduction of the security of the protocol to a security assumption of a cryptographic primitive which itself may rely on the hardness of a computational problem. Attackers are any probabilistic polynomial time Turing Machines (PPTMs). A security proof in this model provides strong guarantees. However, getting proofs in such a detailed model correct is challenging. The symbolic model takes a different approach: messages are modeled as first-order logic terms and cryptographic primitives such as encryption schemes are idealized. Typically, only the intended functionality of an encryption is modeled by an equation such as dec(enc(m,k),k) = m to state that decryption and encryption cancel out when the same key k is used. Crucially, only the properties that are explicitly stated hold and the adversary actions are restricted to those. At the cost of being more abstract this approach is amenable to automation and tools nowadays succeed in proving industrial protocols, such as TLS or Signal.

An Approach to Develop Digital Twins in Industry

The industry is currently undergoing a digital revolution driven by the integration of several enabling technologies. These include automation, robotics, cloud computing, industrial cybersecurity, systems integration, digital twins, etc. Of particular note is the increasing use of digital twins, which offer significant added value by providing realistic and fully functional process simulations. This paper proposes an approach for developing digital twins in industrial environments. The novelty lies in not only focusing on obtaining the model of the industrial system and integrating virtual reality and/or augmented reality but also in emphasizing the importance of incorporating other enabled technologies of Industry 4.0, such as system integration, connectivity with standard and specific industrial protocols, cloud services, or new industrial automation systems, to enhance the capabilities of the digital twin. Furthermore, a proposal of the software tools that can be used to achieve this incorporation is made. Unity is chosen as the real-time 3D development tool for its cross-platform capability and streamlined industrial system modeling. The integration of augmented reality is facilitated by the Vuforia SDK. Node-RED is selected as the system integration option, and communications are carried out with MQTT protocol. Finally, cloud-based services are recommended for effective data storage and processing. Furthermore, this approach has been used to develop a digital twin of a robotic electro-pneumatic cell.

Exploring the power of convolutional neural networks for encrypted industrial protocols recognition

The main objective of this paper is to classify unencrypted and encrypted industrial protocols using deep learning, especially Convolutional Neural Networks. Protocol recognition is important for network security and network analysis. Overall knowledge of industrial protocols and networks is crucial, especially in operational technologies. Five industrial protocol standards are under investigation, namely IEC 60870-5-104, IEC 61850 (MMS, GOOSE, SV) and Modbus/TCP. It is also investigated whether the selected protocols can be recognized in their encrypted version. Furthermore, it is investigated whether this encrypted traffic is recognizable from the use of VPN technology. Three convolutional neural network models were trained to recognize industrial protocols. These networks outperform traditional machine learning in pattern recognition in several areas of classification. By converting the captured traffic into image data that convolutional neural networks work with, differences in the encrypted traffic of different industrial protocols can be recognized. Three scenarios (1D, 2D, PKT) are presented using convolutional neural network models with 1D and 2D architectures. Training, testing and validation data are used to verify each scenario. An accuracy of 96-97% is achieved for the recognition of unencrypted and encrypted industrial protocols. According to the results, 2D convolutional neural network model is faster than 1D and PKT models. The 1D and 2D models are suitable for use in protocol specific networks. Another application of these models can be anomaly detection in these networks. The PKT model is useful in networks with multiple industry protocols because it can evaluate network traffic on a packet-by-packet basis.

Virtual Tool Used for Temperature Monitoring through I2C Protocol

Temperature monitoring is very important both for large geographical and industrial environments, but equally for equipment or subassemblies in a technological process. Monitoring in all cases is important to prevent accidents or the occurrence of major defects. In this paper, a temperature monitoring solution is proposed for a number of up to 8 measurement points located at distances of up to 2 m by means of intelligent integrated sensors capable of transmitting information through the I2C industrial protocol. A flexible hardware structure and software development concept are presented to offer local information and to be integrated into a hierarchical temperature monitoring network.

Анализ методов подготовки и преобразования информации, поступающей в хранилища данных для эффективного управления горнотехнической системой

The article assesses the existing methods of data collection and processing in the industry and proposes an alternative option of data processing to manage mining engineering systems at various stages of their operation. It is demonstrated that the formed volume of information that comes with different frequency rates and requires dedicated processing, structuring and analysis methods forms a system of big data and serves to enhance the efficiency of implementing geotechnical processes. A possible unified structure of data acquisition from digital sources of mining engineering system is presented. An analysis is provided of possible methods to process data for analytical purposes and for searching implicit dependencies and solving problems of predictive analytics. Tools for data collection and storage are proposed to create a unified system of big data analysis for management of mining engineering systems. It is stated that in order to create a unified analytical system for collection of digital data from a mining system, it is necessary to use standard industrial protocols for data collection and storage, for instance MQTT, used as an industry standard in the Industrial Internet of Things (IIoT) systems in accordance with requirements of ISO/ IEC 20922:2016. Storage requires the use of a conventional queue broker architecture, as well as a tool for working with the time series which is required to apply machine learning and big data methods. The approach to data classification in terms of the data acquisition speed proposed in the article makes it possible to standardize the data handling principles. Since the volume of transmitted data does not depend on the frequency of acquiring information from a digital source, it is proposed to transmit all the generated data from a digital source for subsequent search of implicit dependencies between the data. It is noted that application of specific methods and algorithms to analyze data of a mining system depends primarily on the task set which is often formed as a hypothesis to be tested by identifying implicit dependencies between different data sources. In order to improve the efficiency of managing a mining system at all the stages of field development, it is proposed to apply the ELT approach, which can be an important advantage in terms of controlling the technological processes of the mining system in the future.

Time of day-dependent effects of heat treatment on thermal tolerance and sex differentiation in Nile tilapia (Oreochromis niloticus)

The present research aimed to determine the influence of the time of day at which heat treatment (HT) is applied to sexual differentiation and thermal tolerance mechanisms in Nile tilapia (Oreochromis niloticus). To this end, fish larvae were subjected to HT (36 °C) for 12 days at different times of the day during the thermosensitive period (i.e. 11–23 days post-fertilization, dpf). The heat treatment was applied during the light phase (L-HT); during the dark phase (D-HT), or during both light-dark phases (LD-HT). A control group maintained at constant rearing temperature (28 °C, CTRL) was also used. At 25 dpf, thermal tolerance was determined (survival) and whole larvae samples were collected for the mRNA expression analysis (qPCR). There after, all the groups remained at constant temperature until 270 dpf, when some fish were sacrificed and each individual's gonadal lobules were collected for histological and qPCR analyses. At the same time, each experimental group was subjected to acute heat shock (HS; 36 °C for 2 h) in either the mid-light (ML) or the mid-dark (MD) phase. When HS exposure finished, adults' brains were extracted to determine the HS response by qPCR. The expression of the genes involved in female (cyp19a1a, foxl2, era) and male (amh, ara, sox9a, dmrt1) sexual differentiation and thermal tolerance (hsp70, hsp90a, hsp27) in the 25 dpf larvae and adults was studied. The results revealed that all the HT-treated larvae showed an up-regulation of testicular differentiation gene expression and inhibition of ovarian differentiation gene expression, which resulted in a higher percentage of males. The time of day of HT during development influenced thermotolerance in the larval and adult stages, with the lowest survival and highest cellular stress (higher HSPs expression) observed in the larvae of the D-HT and LD-HT groups. These findings reveal the need to further investigate the link between sexual differentiation and thermotolerance daily rhythms in thermal biology research. These results may be useful for optimizing masculinization protocols in the tilapia industry, while ensuring animal welfare and minimizing negative HT effects on fish physiology.

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.