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Abstract Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

Article highlights Automated control system for paper production has been developed, which has increased the accuracy and stability of regulating the weight of paper web. Fiber consumption rates decreased by 2%. The standard deviation of the weight of the paper web decreased by 1.5-2%.

The development of novel technologies using elements of artificial intelligence (AI) in medicine is addressed to practical clinical implementation and provision of key issues, including improvement in the use of routine clinical data, aimed at practical relevance, standardization, confidentiality and patient safety.Aim. To evaluate the effectiveness of the RuPatient electronic heart record (EHR) system in real clinical practice for extracting and structuring medical data.Material and methods. Extraction and recognition of data using EHR from various following sources: outpatient records, statements, routine medical reports, epicrisis and other structured and unstructured medical information based on the developed technology of intelligent text analytics, optical character recognition, for specified words and phrases, and the use of machine learning elements. A particular criterion for evaluating the effectiveness of EHR is the time spent on filling out electronic medical records compared to real clinical practice.Results. The time of entering and processing information by the recognition system of medical documentation included in the RuPatient EHR was shorter than in standard practice (20,3±1,4 minutes, 25,1±1,5 minutes, respectively, p

Abstract. The software development life cycle (SDLC) has seen the evolution of numerous models, each tailored to meet specific project needs. Among these, the Waterfall Model has been a longstanding, traditional approach, characterized by its linear and sequential phases. In contrast, the PcD.UcT Model, a relatively newer framework, advocates for a more iterative and user-centered approach. This paper presents a comprehensive review of the Waterfall Model, evaluating its strengths and limitations in modern software development. It contrasts these findings with the PcD.UcT Model, analyzing how its principles better align with contemporary project demands, particularly in terms of flexibility, user engagement, and iterative refinement. Through this comparative analysis, the paper aims to provide insights into the applicability and effectiveness of each model, offering guidance for software development teams in choosing the appropriate methodology for their projects. Since the waterfall development methodology, the key requirements in system development strategies have shifted from processes to users. Because the process as well as the customers are both equally important, employing either a predictive or an adaptive methodology is extremely difficult. The primary goal of this work is mainly combining and then evaluating all the relevant methodologies in order to create a HydroGIS that accurately automates the complexities of the hydrology process in a HydroGIS environment while meeting all user requirements.

In recent years, companies worldwide have faced a rapid pace of digitization and automation, which has led to change and adaptation of business models. From this point of view, new technologies have revolutionized the field of accounting and auditing, having both positive and negative effects on companies and employees. This paper highlights how changes brought about by technological progress influence the accounting and auditing profession and the role of other factors in this direction, using a qualitative method based on semi-structured interviews. The study results show that the benefits are visible at the company level. However, certain obstacles still exist, such as employees' resistance to change, the size of the initial costs or the systems used. On the other hand, professionals expect some entry-level jobs to disappear. Instead, other opportunities will be available for practitioners in the field. In this sense, universities will have a unique role in training the new generations by developing skills for the digital age. The present study may be of interest to researchers examining related issues. From a practical point of view, this paper could be helpful to professionals as it highlights several current needs of the business environment due to the impact of technological innovations.

In an era where artificial intelligence (AI) is rapidly shaping the future of technology, it is becoming increasingly integrated and utilized across various fields, unlocking a wide range of applications and use cases. Among these, the field of cybersecurity will benefit significantly, as AI has the potential to enhance traditional methods of detection and prevention. This paper aims to present the latest advances in AI technology, exploring how AI can be synergistically connected with cybersecurity and highlighting the types of applications already developed in this area. Additionally, this paper will discuss the ethical considerations and current challenges associated with the use of AI to ensure a more secure environment.

As artificial intelligence (AI) is becoming increasingly integrated into critical infrastructures, it brings about both transformative benefits and unprecedented risks. AI has the potential to revolutionize the efficiency, reliability, and responsiveness of essential services, but it can also offer these benefits along with the vulnerability to a growing array of sophisticated adversarial attacks. This paper explores the evolving landscape of adversarial threats to AI systems, highlighting the potential of nation-state actors to exploit these vulnerabilities for geopolitical gains. A range of adversarial techniques is examined, including dataset poisoning, model stealing, and privacy inference attacks, and their potential impact on sectors such as energy, transportation, healthcare, and water management is assessed. The consequences of successful attacks are substantial, encompassing economic disruption, public safety risks, national security implications, and the erosion of public trust. Given the escalating sophistication of these threats, this paper proposes a comprehensive security framework that includes robust incident response protocols, specialized training, the development of a collaborative ecosystem, and the continuous evaluation of AI systems. The findings of this study 11 underscore the critical need for a proactive approach to AI security in order to safeguard the future of critical infrastructures in an increasingly AI-driven world.

The research aimed to explore the potential of advanced machine learning (ML) algorithms in clinical and biomedical research. The significance of frameworks like generative adversarial networks (GANs), autoencoders, and autoregressive models in tackling the issues of representation learning and the quality of generated content is highlighted. This paper also presents a proposed system architecture for integrating generative artificial intelligence (GenAI) into healthcare processes. This architecture encompasses components for data ingestion, preprocessing, model training, image enhancement, diagnostic analysis, and user interfaces for healthcare providers and patients, utilizing advanced artificial intelligence (AI) models. The paper underscores the necessity of robust data governance frameworks, ethical guidelines, and secure infrastructures to mitigate the associated risks. By fostering collaborative AI-human systems and continuously assessing ethical implications, the healthcare industry can fully exploit GenAI's potential to improve patient outcomes and operational efficiency.

Abstract This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance.

Abstract Mobile bed to move patients in hospitals is a critical aspect in a healthcare system. Typical hospital beds often require significant manpower and impose physical stresses on caregivers. This paper explores potential solutions to improve hospital bed mobility, focusing on the integration of robotic technologies. Existing literatures on the subject are reviewed, including studies on hospital bed design structures, ergonomic considerations, and implementations of robotics. Different types of wheels suitable for robot movements are discussed, covering conventional wheels, omniwheels, mecanum wheels, swerve drives, and powered fifth wheels. Each wheel type is described and its advantages and disadvantages are highlighted. The comparison of these wheel types helps identify the most suitable option for modifying hospital beds while maintaining their maneuverability. Furthermore, the paper draws inspiration from many applications of mobile robots in other industries, such as automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), to explore potential innovations for the healthcare. By leveraging robotic technologies, hospitals can enhance efficiency and alleviate the burden on healthcare workers.

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system’s various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems.

This conceptual article theorises the tensioned interplay between digitalization, datafication and automation and subjectness in education by asking what intensifying datafication and automation means for teacher–student relationships and how we understand and approach education. Theoretically, the paper draws on Buber’s ideas of the dialogical I–Thou and objectified I–It as the key forms of human relationships. The core argument is that increasing datafication and automation steers the teacher–student relationship towards an objectified I–It relationship instead of the dialogical I–Thou relationship, which Buber (and others such as Biesta, another main influencer of the present paper) saw as the ideal. Literature-informed examples of various forms of educational datafication and automation are provided to support and concretise the arguments.

Energy consumption prediction in buildings is a major issue in the field of energy efficiency and resource management. In recent decades, the use of Artificial Intelligence (AI) has become an increasingly common approach to improve the accuracy and reliability of these predictions. In this paper, the most used digital technologies such as: AI, Big Data, Internet of Things (IoT), Blockchain, Cloud computing and 5G, which can fundamentally transform the way energy consumption predictions are made in buildings, are presented; the different types of data used in energy consumption prediction are analysed, such as weather data, energy use data and building characteristics; the various AI methods and algorithms are presented, e.g. neural networks, decision trees, support vector and machine learning algorithms, used to improve prediction accuracy. This paper focuses on a conceptual energy prediction model developed for the project "Intelligent system for predicting energy consumption in buildings (PRECONERG)", a project in the first stage of development.

This paper investigates the utilisation of ChatGPT (Generative Pre-trained Transformer) in the field of Facial Emotion Recognition (FER), streamlining development processes and making them smoother and faster. With ChatGPT, coding and debugging happen fast, leading to the swift creation of performing models in mere minutes. It highlights the transformative potential of integrating ChatGPT into image processing workflows, accelerating progress in computer vision, graphics, and multimedia. This paper also shows the importance of word choice and developer skill in finding the right balance between speed and accuracy. It sets the stage for further investigations into advanced Large Language Models (LLMs) and finetuning parameters, leading towards faster, more efficient image processing techniques.

One key role in industrial control systems (ICSs) is known as Programmable Logic Controller (PLC). However, with the development of the Internet of Things (IoT), PLCs have become exposed to an increasing number of attacks, which may cause malfunctions of the whole ICS. Thus, it is necessary to identify potential attacks on PLCs and propose effective solutions to mitigate them. Unfortunately, to date, there have not been significant efforts made to provide a detailed overview of existing works on PLC security. With such a concern in mind, in this paper, we focus on summarising PLC security from different components running at different layers of a PLC architecture. We first review the framework of PLCs; then, we discuss several models when considering PLC security. After that, we provide an overview of existing attacks on PLCs and general solutions to those issues from different perspectives. Lastly, we conclude this paper with an overview of future research areas in PLC security.

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

The organisational focus on workplace automation is fuelling the evolution of the labour market towards task focused work systems, and an emerging gig economy. This evolution creates new demands on organisational leaders to introduce flexible and agile management techniques, whilst maintaining a strategic focus on upskilling and reskilling their employees for a digitalised world. We revisit our previous GJSD paper, “Mind the leadership gap”, to further emphasise these aspects of the changing work environment and introduce 1. Their potential influence on organisational belonging, and 2. The resultant impact on leadership styles and skills. This paper presents a short synthesis of the complex challenges faced by leaders within such an operating environment. This is followed by four further proposals for the future research agenda along with two suggested research methods which enhance our previous call to action on the leadership gap.

The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature.

The paper shows the development of technologies and tools of mechanization and automation in livestock farming for the period from 1930 to the present. (Research purpose) Given the cyclical nature of processes and the nuanced interpretations of scientific heritage, it is necessary to investigate the historical experience and contributions of scientists to the development and practical implementation of mechanization and automation tools in livestock farming. (Methods and materials) The paper examines the primary stages of mechanization and automation in cow milking, starting with the development of the first Soviet three-stroke milking machine DA-3. It considers the contributions made by specialized institutes of the All-Russian Academy of Agricultural Sciences and individual scientists in formulating testing methodologies for milking machines. Additionally, it explores the development and adoption of novel electromechanical tools for dairy farms. (Results and discussion) Considered in this paper are the pioneering efforts in the creation of the first milking parlors. These include the stationary herringbone type machines with a parallel-pass design, spearheaded by V.S. Krasnov, V.F. Korolev, V.P. Larin, V.P. Pokhvalensky, and A.N. Dormidontov, and the development of mobile carousel parlors of the «rotating herringbone» type, engineered by the design bureau of the Siberian Research Institute of Agriculture under the supervision of I.I. Teslenko, N.V. Krasnoshchekov, K.S. Shapovalov, N.K. Vazenmiller, A.V. Goldenfang. (Conclusions) The paper notes the significance of adopting a systematic approach to the developing of machinery for livestock farming mechanization. This approach was actively pursued by N.M. Morozov, the member of the Russian Academy of Sciences. A significant milestone in the advancement of the theory and practice of animal husbandry processes was the pioneering work in developing and implementing technology for flow-conveyor animal service, headed by Member of the Russian Academy of Sciences L.P. Kormanovsky. The paper reveals the necessity of conducting a historical practices from previous generations. This approach helps to develop modern models of machinery and equipment and avoid mere replication of foreign designs. It also facilitates independent research and development efforts, incorporating insights from the accumulated experience of utilizing equipment and technologies in Russia.

Earthquake misinformation has not only a significant impact on crisis management, but also on trust in scientific information and institutions. As a global center for seismic information, the Euro-Mediterranean Seismological Center (EMSC) has developed a strategy to combat false information related to earthquakes, particularly on Twitter (now known as X). This strategy includes the automation of prebunking tweets to prevent the spread of false information and, particularly unreliable claims of earthquake predictions. In the field of false information, predictions of earthquakes have a particular characteristic: their appearance follows a specific dynamic and the attention paid to them is predictable, which makes the automation of countermeasures (prebunking and debunking) both possible and relevant. Therefore, the objectives of the EMSC’s automatic tweets are to warn people in advance, particularly those who may be vulnerable to earthquake misinformation, while filling the information void with verified, scientifically-based information in the meantime. This paper seeks to examine the usefulness of such a tool. It does so by analyzing users’ engagement with these tweets. In addition, it focuses on two case studies, the seismic sequence in Turkey following the M7.8 earthquake on February 6, 2023 and the M6.8 Morocco earthquake on September 8, 2023. The results show engagement with the automatically generated tweets but an even greater interaction and engagement with manual tweets, highlighting the importance of a holistic approach. Recommendations include optimizing the visibility of the tweets, extending efforts beyond social media, and exploring alternative platforms. The paper advocates for a comprehensive strategy, combining automated tools with manual engagement and leveraging diverse communication channels to take into account cultural differences.

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

This paper assesses empirically the COVID-19 effect on businesses and the potential dynamic changes regarding post-COVID-19 automation and technology penetration using various logistic regression models. A field survey was used to collect the necessary data for testing various hypotheses. This study demonstrates the severity of the pandemic on businesses and how it has changed their perspectives on technology as a critical aspect of survival and future success. The results showed that capital-intensive firms are more resilient to the crisis. In addition, the firms that were affected severely in terms of employment due to the pandemic believe that technology will significantly impact hiring, investment, and value added. This paper investigates a unique phenomenon represented by COVID-19, its impact on businesses in a resource-rich context and their responsiveness concerning technology deployment and automation.

ABSTRACTThe inherent randomness in economic factors causes complex and irregular behaviour that affects financial system stability and economic growth. Such chaotic behaviour can make it difficult to synchronize financial systems. The chaotic finance system synchronization precision maintains financial stability and economic growth. In this paper, the controller design procedure assumes that the financial system is exposed to unknown bounded exogenous disturbances and model uncertainties. This research proposes a novel direct adaptive control strategy that achieves robust synchronization of two identical four-dimensional finance chaotic (FDFC) systems. The proposed controller establishes a faster, smoother synchronization error vector convergence to zero. The controller design procedure does not eliminate the closed-loop's nonlinear terms and is independent of the financial system parameters. These controller's attributes accomplish the closed-loop robust performance. Further, this controller uses real-time estimates of unknown model uncertainties and bounds to compensate for unknown exogenous disturbances. Computer simulation results and proofs of theoretical analysis based on the Lyapunov stability theory confirm that the proposed control technique compels the error vector trajectories to the origin in a short transient time with less active oscillations for all signals. The paper includes comparative computer simulations for verifying the theoretical findings.

The paper highlights the importance of clarifying scientific terminology to delineate the exclusion of humans from technical systems, in particular the concepts such as «mechanization,» «automation,» «robotization,» and «digitalization» that are commonly used in scientific literature without a precise definition of their essence and distinctions. (Research purpose) The paper aims to provide clarity and systematic categorization of terms employed to describe the exclusion of humans from agricultural technical systems. (Materials and methods) The paper employs the Theory of Inventive Problem Solving (TRIZ), the Theory of Decision Making, and the System Method as theoretical frameworks. (Results and discussion) The paper provides a comprehensive clarification of the conceptual meanings attributed to terms such as mechanization, automation, robotization, and digitalization. It outlines their classical (Aristotelian) definitions and systematically categorizes a broad spectrum of related concepts, including mechanization, automation, robotization, intellectualization, digitalization, cyber-physical system, precision agriculture, digital agriculture, and smart (intelligent) agriculture. The obtained results are compared utilizing the approach of the Food and Agriculture Organization of the United Nations (FAO). (Conclusions) It is concluded that the concept of mechanization, in contrast to the FAO approach, does not encompass farming reliant on manual labor and animal traction. It is determined that robotization necessitates intellectualization, whereas automation does not necessarily require it. Furthermore, digitalization is demonstrated as a means of implementing automation, robotization, intellectualization, and optimizing the operation of technical systems.

Abstract This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

Virtual commissioning (VC), defined as the simulation and testing of systems in a virtual environment before physical implementation, plays a key role in addressing the challenges of integrating and validating complex systems efficiently and effectively. This paper focuses on the topic of virtual commissioning, summarizing and organizing existing research in the field. The paper provides a comprehensive overview of various design methods and technologies currently in use. A case study of virtual commissioning is also presented within the area of the Cyber-Physical Manufacturing Systems Laboratory of the Széchenyi István University, detailing the solution steps taken. Drawing on both research and practical experience, the paper proposes a novel framework to support virtual commissioning design, referred to as the “Virtual Commissioning House” (VCH). The methodology is evaluated through comparisons with existing virtual commissioning solutions, demonstrating its effectiveness.

Scientific research leads the scientific progress based on which information technology (I.T.) professionals drive technology changes, impacting our daily life, business, and work models. The same inventions are impacting the work of the software developers, the ones responsible for building this technology. While I.T professionals are building new tools for automation used in the industry, the work of I.T. professionals is significantly impacted by the tools they build. Paying heed to these considerations, the paper intends to bring forward qualitative research based on semi-structured interviews with 18 I.T. professionals. The research purpose is to scrutinize to what extent the workers in the software development industry are aware of the transformations happening in their profession, how they see the future of their jobs and how they prepare for that future. The I.T. engineers taking part in the interviews see the impact of technology in changing their work models, making them more productive rather than a threat to their jobs. They also understand the importance of continuous learning, keeping up with technological changes and soft skills.

Abstract. The development and application of automated technologies in the agricultural sector have significantly transformed the management and monitoring of farm animals. This paper presents a comprehensive analysis of existing technical systems for measuring the physiological parameters of farm animals, particularly focusing on cows. The primary goal of these technologies is to enhance the accuracy and efficiency of health monitoring and to optimize livestock management through continuous data collection and analysis. Key technological solutions include the use of wireless data transmission systems, radio monitoring devices, and unmanned aerial vehicles (UAVs). These technologies enable real-time tracking of animal health parameters such as body temperature, heart rate, movement patterns, and other critical indicators. The use of various sensors, including ear, neck, and intra-body sensors, plays a crucial role in collecting primary physiological data. These sensors are integrated into a broader system that includes radio modules with built-in controllers for converting raw data into digital formats, enabling wireless transmission to central systems for processing and storage. The radio monitoring of animals allows for the continuous tracking of physiological and behavioral parameters across large areas, providing crucial insights into their well-being. It is particularly beneficial in detecting early signs of disease or health issues, ensuring timely intervention, and preventing potential economic losses for farmers. The integration of unmanned aerial vehicles (UAVs) further expands the capabilities of monitoring systems, enabling the visual assessment of animals' behavior and movement across vast pastures. Equipped with video cameras and data relays, UAVs collect video footage that is transmitted to a central processing unit for real-time analysis and storage. The combined use of radio monitoring and UAV technology forms a comprehensive framework for livestock management, allowing farmers to make data-driven decisions, optimize resource allocation, and improve overall herd health and productivity. By leveraging these automated systems, it becomes possible to reduce the dependency on manual labor, minimize human error, and ensure that animals are closely monitored without the need for constant physical presence. This represents a significant advancement in modern animal farming, aligning with global trends toward smart farming and precision agriculture. In conclusion, the integration of automated physiological monitoring technologies in farm animal management presents an innovative solution for enhancing animal welfare, improving farm efficiency, and reducing operational costs. These systems hold considerable potential for further advancements in the field of animal health monitoring, ultimately contributing to more sustainable and productive farming practices.

Medical Thermography image is used for the detection of breast cancer at an earlier stage. Thermography image shows the temperature change in the body due to cells. The metabolic rate of cancer cells is high compared to normal cells. A high metabolic rate increases the blood flow in cancer cells. High blood leads to changes in body temperature. The change in body temperature is used for cancer cell detection at an earlier stage. However, T1-stage cancer cells are smaller and have small temperature differences undetected with thermography. In this paper, a T1-stage cancer cell is heated by an external source; then, thermal images are acquired for earlier detection of small-size cancer cells. External heat source amplifies T1 stage cancer cell temperature. Amplified cancer cell images are analyzed using the proposed Multiwavelet-Deep Denoised Convolutional Neural Network (MWTDnCNN) algorithm for T1 cancer cell detection. Amplified T1 stage cancer cell has higher thermal conductivity (k) and heat capacity (Cp), which helps to detect T1 cancer cell tissue due to the enhanced pixel feature. The proposed MWTDnCNN algorithm has a T1-stage cancer cell detection accuracy of about 98% compared with traditional algorithms. The proposed MWTDnCNN algorithm detects T1-stage cancer of size 1.29 mm.

As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their security. That's where image security techniques come in. In this paper we have developed a new approach to secure e-patient records like DICOM images. By combining the redundant discrete wavelets transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD). We developed a robust and dual watermarking scheme. This scheme uses multiple watermarks, including Electronic Patient Record (EPR) as text and images, to ensure high-level authentication. In order to attain a balance between imperceptibility and robustness, a PSO-based optimization of scale factor is employed and Turbo code is utilized to encode the EPR and minimize channel noise. Additionally, the marked image undergoes encryption using a 3D chaotic-based encryption technique, and the extracted watermark is denoised through a deep neural network. The result shows that the proposed scheme is both secure and reliable. With this dual watermarking scheme, we have made great strides in securing e-patient records.

This paper proposes a novel approach that leverages a hybrid deep learning framework called the Squirrel Search-optimized Attention-Deep Recurrent Neural Network (SS-ADRNN) to optimize the management of charging stations, ensuring efficient resource allocation while safeguarding user data and minimizing operational costs. The SS-ADRNN model incorporates squirrel search optimization, which is inspired by the foraging behaviour of squirrels, to dynamically adjust charging station operations based on environmental conditions and demand patterns. Additionally, attention mechanisms are employed to prioritize relevant input features, enabling the model to focus on critical information during decision-making processes. Deep recurrent neural networks (RNNs) are utilized to capture temporal dependencies in charging station data, allowing for more accurate predictions and adaptive control strategies. Experimental evaluations demonstrate the effectiveness and feasibility of the proposed SS-ADRNN-based approach in real-world scenarios. The results showcase significant improvements in the detection of malicious traffic and cost minimization compared to traditional charging station management methods. Overall, this research contributes to advancing the field of intelligent charging station optimization, offering a robust and adaptable solution for EV and UAV charging infrastructures that prioritize both security and operational efficiency.

Some traditional cryptographic techniques, like the Secure Hash Algorithm (SHA-256 for hashing), Rivest-Shamir-Adleman (RSA/Elliptic Curve for signing) and Advanced Encryption Standard (AES for encryption), perform well on systems with good hardware memory and processing capabilities. However, these techniques engage in conflict to keep up with the world of sensor networks and embedded systems. Lightweight cryptography plays a major role in security constraints, especially in resource-limited devices such as RFID tags, smart cards, sensor nodes and IoT. This paper proposes a flexible hardware architecture of lightweight m-Crypton block cipher for high-speed resource-constrained applications. The proposed architecture enables a single architecture appropriate for the many encryptions' key sizes. Therefore, the proposed architecture changes the security level in resource-constrained applications by integrating several key sizes into a single design. Furthermore, this architecture outperformed the conventional block ciphers in terms of throughput-to-area ratio achieving a 10.37 throughput-to-area ratio better than other lightweight block ciphers. The proposed design can be used in high bandwidth applications, high-end RFID and IoT smart devices. Hence, the proposed design demonstrates that increasing the speed of cipher implementation results in more plaintext transformations into ciphertext. All results have been verified and simulated for several Xilinx design suite families.

Partial shading conditions (PSCs) may be unpredictable and difficult to forecast in large-scale solar photovoltaic (PV) systems. Potentially degrading the PV system's performance results from numerous peaks in the P–V curve caused by PSC. On the other hand, the PV system must be run at its maximum power point (GMPP) to maximize its efficiency. Swarm optimization strategies have been employed to detect the GMPP; however, these methods are associated with an unacceptable amount of time to reach convergence. In this research, an innovative grey wolf optimization, abbreviated as NGWO, is presented as a solution to overcome the shortcomings of the standard GWO method, which includes long conversion times, a rate of failure, and large oscillations in a steady-state condition. This paper seeks to address these issues and fill a gap in research by enhancing the GWO's performance in tracking GMPP. The original GWO is modified to incorporate the Cuckoo Search (CS) abandoned process to shorten the time it takes for effective adoption. Based on the simulation finding, the proposed IGWO method beats other algorithms in most circumstances, particularly regarding tracking time and efficiency, where the average tracking time is 0.19s, and the average efficiency is 99.86%.

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

The paper describes the influence of the magnetizing inductance of the transformer on zero-voltage switching of the primary transistors in a soft-switching full-bridge dc-dc converter with a controlled rectifier and turn-off snubber. The optimized version of the transformer allows for achieving a full zero-voltage and zero-current turn-on of the primary transistors in a full-bridge soft-switching DC-DC converter. The analysis is verified by measurements on a laboratory model of the converter. The influence of undischarged output capacitances on the efficiency is measured as well. Also, the impact of the GaN transistors on the zero-voltage switching is discussed.

Abstract We present analytic techniques for parametric integrations of massive two-loop four-point Feynman integrals at high energies, and their implementation in the toolbox AsyInt. In the high-energy region, the Feynman integrals involving external and internal massive particles, such as the top quark, Higgs and vector bosons, can be asymptotically expanded and directly calculated in the small-mass limit. With this approach, analytic results for higher-order terms in the expansion parameter and the dimensional regulator can be obtained with AsyInt. These results are important ingredients for the two-loop electroweak and QCD corrections for 2 → 2 scattering processes in the large transverse momenta region, which is relevant to both precision collider phenomenology and new physics searches at current and future high-energy colliders. In this paper, analytic results of representative planar and non-planar Feynman integrals are presented.

Abstract This paper explores the application of computer vision and mathematical modeling to analyze the intricate movements involved in weaving a traditional farming tool, the winnowing basket. By utilizing OpenPose algorithms, the study simplifies and visualizes the craftsmen's motions, particularly focusing on wrist movements. Video data of craftsmen in Chiba, Japan, creating Kizumi (place name) winnowing baskets is used as the basis for analysis. The extracted information is used to generate 2D motion trajectories of the wrist, allowing a comparison between beginners who watched parsed videos and those who watched the original videos in terms of skill acquisition and learning time. By visualizing human body behavior and combining statistical results, this study demonstrates the potential of artificial intelligence techniques such as computer vision for observing repetitive human movement and inheriting traditional skills.

For a large number of citizens dealing with public authorities can feel like a harrowing experience. With a large amount of bureaucracy and ever-changing rules and procedures, it can be quite confusing for people to achieve their goals. The aim of this paper is to prove that a chatbot can be trained with minimal coding experience, by having a robust dataset created by people with domain-specific knowledge. This would empower every public institution to customise a helper chatbot. This chatbot would not only provide common information about required documents for whatever needs to be done, but also more general information pertaining to the specific aspects of that institution, such as its location, how to get to it, or public transportation connections nearby. The aim is to provide the necessary steps so that any public institution willing to undertake this digital transformation step can do so without much outside help or technical know-how in the field of Artificial Intelligence.

The last decade has witnessed the exponential growth of Virtual Reality (VR) as an affordable, powerful, and disruptive technology in a variety of fields, among which, notably, are healthcare and well-being. Particularly for the elderly people, VR has already been demonstrated, by a plethora of experiments and studies, as extremely effective in supporting a multitude of therapeutical interventions, from neuromotor and cognitive recovery and maintenance to mental health and wellbeing and socialisation. However, due to the novelty of the technology, the scientific and industry communities are missing both an overarching perspective covering all these intervention areas, as well as a common understanding of the results, challenges, limitations, possibilities, specificity, best practices, design approaches, and integration with traditional therapies, for each area. This hinders the development of commercial-level solutions and widespread adoption in clinical guidelines. This paper aims to cover these gaps, by identifying and analysing five of the most relevant VR intervention areas for elderlies, providing an overview and examples, for each area, and concluding with cross-area perspectives and pinpoints of the most needed research topics, to support the widespread adoption of VR-based interventions for elderly healthcare and wellbeing.

This paper aims to analyze the classic approach towards the Workplace ecosystem and make a set of general recommendations, business-agnostic which applied, allow organizations to reach a Digital and Integrated Workplace. An overview is presented on the key components of a reliable IT department, with a focus on the critical infrastructures across all levels: Front-End, Middleware, and Back-End systems. It starts by gathering the most important innovation methods in the IT domain. Then, our research follows on a concrete example of a company that underwent the implementation of these changes and check how effective they have proven to be. The research focuses on the adoption of these measures and how they has been reflected in their employee satisfaction level. Finally, the most interesting Key Performance Indicators from multiple standpoints are advocated: resource allocation (cost, time, manpower), user experience, and the impact of emerging technologies based on AI and service governance. While it is clear that all of these riders' implementation depends mainly on the budget, according to Gartner, 62% of IT services buyers confirmed that their organizations are willing to pay an extra 11% to 20% of the already allocated budget for top premium IT services, tailored to their custom needs with a focus on the user experience.

The public administration constitutes the mechanism through which government policies and public programs are implemented for citizens. Coherent communication and the quality of services provided are extremely important and must take place in a safe and collaborative environment, both for citizens and the organizations with leadership role involved. The rapid development of emerging technologies has determined a huge innovation potential in the virtual environment, with results especially in the economic, educational and health fields. This paper aims to highlight the potential of Blockchain technology and the Metaverse space in the field of public administration in the coming years, starting from the motivation that of all categories of citizens must benefit from high quality administrative services in a fully immersive world. The technological and application aspects of Blockchain technology addressed in this research can support the digitization of public services and the development of clear governance models. Potential public services in the Metaverse have been suggested along with the role of Blockchain technologies as an alternative to traditional government services.

Abstract The manipulation of deformable linear objects (DLOs) such as ropes, cables, and hoses by robots has promising applications in various fields such as product assembly and surgical suturing. However, DLOs are more difficult to manipulate than rigid objects because their shape changes during manipulation. Furthermore, preventing a DLO from colliding with the environment is important to prevent it from becoming entangled and causing shape control to fail. In this paper, we proposed an obstacle avoidance and shape control scheme for DLOs based on differentiable simulation that does not require prior data or a specialized controller. First, we established a dynamic model of the DLO that allows for both forward dynamics transfer and error backpropagation to obtain gradients. Then, we employed model predictive control to optimize the embedded neural network for predicting the actions that would manipulate the DLO. Finally, the control scheme was made applicable to DLOs with different material properties by allowing online adaptation of the model parameters essential to deformation during manipulation. Simulations and real-world experiments demonstrate that the proposed control scheme could manipulate the DLO stably and accurately to avoid obstacles and achieve the goal state. In addition, the online adaptation of parameters helped mitigate the sim-to-real gap.

The development of digital analysis and its impact on the country’s economic sector underscores the relevance of its study and evaluation. In the context of a rapidly changing economic paradigm, the study of this topic acquires strategic importance, emphasizing the need for an analytical approach to the adaptation and development of business processes in the context of digitalization. The article is devoted to an academic review of the stages of development and the role of digital analysis in the modern economy, the study of key aspects of digital analysis, as well as an assessment of its prospects in the context of the modern corporate environment. The purpose of scientific research is to analyze the evolution of digital technologies, identify factors contributing to the formation of digital analysis, as well as assess the impact of this methodology on business processes, strategic management and overall economic dynamics. In writing the paper, analysis and synthesis methods were used to study the evolution of digital technologies, as well as research methods, such as comparative analysis and expert assessments, to identify factors that contribute to the formation of digital analysis and assess its impact on various aspects of the economy. Scientific research has revealed the key stages of the emergence of digital analysis, revealing its essence and role in the modern digital economy. Significant aspects of the impact of digital analysis on business processes and overall economic dynamics were identified. The findings highlight the importance of digital analysis in today’s corporate environment and its potential to improve business processes and strategic decision-making in a rapidly changing economic environment. Further research in this area may include a deeper analysis of the specific applications of digital analysis and its impact on various sectors of the economy. The scientific novelty of the article lies in a comprehensive study of the stages of development, the essence and role of digital analysis in the modern economy, its impact on business processes, strategic management and general economic dynamics. Previous studies were most often limited to the technical aspects of digital analysis, this article is an in-depth analysis of not only the evolution of digital technologies, but also the factors that contribute to the formation of this methodology

Digital twins (DTs) are an emerging digitalization technology with a huge impact on today’s innovations in both industry and research. DTs can significantly enhance our society and quality of life through the virtualization of a real-world physical system, providing greater insights about their operations and assets, as well as enhancing their resilience through real-time monitoring and proactive maintenance. DTs also pose significant security risks, as intellectual property is encoded and more accessible, as well as their continued synchronization to their physical counterparts. The rapid proliferation and dynamism of cyber threats in today’s digital environments motivate the development of automated and intelligent cyber solutions. Today’s industrial transformation relies heavily on artificial intelligence (AI), including machine learning (ML) and data-driven technologies that allow machines to perform tasks such as self-monitoring, investigation, diagnosis, future prediction, and decision-making intelligently. However, to effectively employ AI-based models in the context of cybersecurity, human-understandable explanations, and their trustworthiness, are significant factors when making decisions in real-world scenarios. This article provides an extensive study of explainable AI (XAI) based cybersecurity modeling through a taxonomy of AI and XAI methods that can assist security analysts and professionals in comprehending system functions, identifying potential threats and anomalies, and ultimately addressing them in DT environments in an intelligent manner. We discuss how these methods can play a key role in solving contemporary cybersecurity issues in various real-world applications. We conclude this paper by identifying crucial challenges and avenues for further research, as well as directions on how professionals and researchers might approach and model future-generation cybersecurity in this emerging field.

The paper presents the results of a study showing the accuracy of the determination of aircraft position coordinates based on the SPP (Single Point Positioning) solution in the GLONASS (Globalnaja Navigatsionnaya Sputnikovaya Sistema) system. For this purpose, the paper develops and implements an algorithm for the correction of position errors as parameters describing positioning accuracy. The proposed algorithm uses position error values determined for a single GNSS (Global Navigation Satellite Systems) receiver, which are joined in a linear combination to determine the positioning accuracy of the aircraft. The algorithm uses linear coefficients as an inverse function of the number of GLONASS satellites being tracked by the GNSS receiver. The developed algorithm was tested for GLONASS satellite data recorded by Topcon HiPer Pro and Javad Alpha geodetic receivers, during a flight test carried out with a Cessna 172 aircraft around the military airport in Dęblin. Navigation calculations were carried out using RTKLIB v.2.4.3 and Scilab v.6.0.0 software. On the basis of the tests carried out, it was found that for single Topcon HiPer Pro and Javad Alpha receivers, position errors were up to ±11.4 m. However, by using the position error correction algorithm for both receivers, GLONASS positioning accuracy is up to ±3.6 m. The developed algorithm reduces position errors by 60-80% for all BLh (B- Latitude, L- Longitude, h- ellipsoidal height) coordinates. The paper shows the possibility of testing and implementing the proposed mathematical algorithm for the SPP solution in a GPS (Global Positioning System) navigation system. In this case the position errors from the GPS SPP solution range from -0.9 m to +0.9 m for all BLh coordinates. The obtained results showed that application the GLONASS and GPS system in air transport is important. The algorithm used in this work can also be applied to other global GNSS navigation systems (e.g. Galileo (European Navigation Satellite system) or BeiDou (Chinese Navigation Satellite System)) in air transport and navigation.

Data is being stored electronically increasingly more, and the importance of databases has therefore increased significantly. Given that the data collected is used for various reasons and the sensitivity of the data can differ, the importance of database security is crucial. Although, database security has been on the “agenda” of security experts for a long time, technologies have changed and databases have evolved accordingly. In this paper, the aim is to offer a refreshed perspective of the security measures implemented in databases nowadays, with a comparison study of two popular databases: Oracle and Microsoft SQL. The comparison will provide the strengths and weaknesses of both database systems after analyzing the authentication, authorization, encryption, auditing and user management features provided by both, to help determine which Database Management System has a more comprehensive asset of tools to protect its internal data. Furthermore, this paper can also offer a guide for database administrators, to help them choose the best strategy and tools to secure their databases.

The Metaverse concept describes a virtual collective space where users engage with simulated or computer-generated environments, enabled by cutting-edge technologies such as Virtual Reality (VR), Augmented Reality (AR), Blockchain, Artificial Intelligence (AI), and 5G Networks. This convergence allows the creation of complex virtual worlds, enhances user interactions, enables dynamic environments, facilitates seamless integration across platforms, and ensures scalability and persistence, alongside several privacy, security, ethical and legal challenges to be considered. The constant advancement of these technologies is pivotal in shaping a future where the line between physical and virtual realities blurs, unlocking limitless possibilities in realms such as communication, entertainment, education, and business. This leads to novel opportunities for exploration and user engagement. This paper aims to present an overview of the leading technologies driving the transition from the current Internet to the Metaverse, enabling users to interact naturally and intuitively with digital objects and environments. The study explores well-established interactive simulation technologies like virtual reality and augmented reality, along with emerging technologies like somato-sensory interfaces, holographic imaging, and brain-computer interaction interfaces. Emphasizing their potential, the paper demonstrates how these technologies can offer users unparalleled levels of involvement in interactive activities spanning gaming, socializing, education, and business.

Year to year, vehicles are becoming more advanced, and manufacturers offer newer support systems. Progressive tech-nology development must be followed by relevant sociological changes, including establishing a proper user aware-ness level. Even though compulsory driver training, required before obtaining a license, consists of selected support features, e.g. ABS (Anti-lock Braking System), it does not provide novice drivers with the necessary practical skills and knowledge of all automation features available on the market. To reduce the human error factor, the European Parlia-ment adopted new regulations, including minimum safety requirements for new vehicles. This paper identifies the gap between the current approach toward teaching automation and necessary changes that should be made to ensure road safety. It provides an overview of ADAS functions allowed to be used during driving license exam of category B in different European countries.Moreover, the publication contains results of work carried out under the Trustonomy project. Outcomes obtained from the questionnaires were used to develop new driver training curricula. The publication discusses the developments of a survey conducted among 83 Polish drivers and 91 car fleet managers. The paper reveals their attitude and expectations towards driver training. The results indicate that despite the awareness of ADASs positive impact on safety (80% of drivers vote, 96% of car fleet managers votes), many people still didnt take part in any training and still do not know how to use systems properly. Even more worrying is the fact that more than 50% of drivers admitted they acquired knowledge about system operation based on their own mistakes. Many responders expressed their interest in acquiring new knowledge. This situation indicates an urgent need to introduce changes to the driver training system. Therefore, the publication highlights different regulatory boundaries across Europe and stresses the need to update existing curricula to introduce proper automation-related training.

Traffic capacity is an important index to measure the operation efficiency of expressway toll stations. In order to pro-vide relevant theoretical support for accurately evaluating the congestion degree and service level of toll stations, this paper establishes a traffic capacity calculation model for the square in front of expressway toll stations based on the traditional cellular transmission method. Firstly, by dividing the square in front of the station into regular cells, the process of calculating the traffic capacity is simplified; Secondly, the capacity model of the square in front of the station is established based on cellular transmission, and a large amount of data is collected through the monitoring videos of several toll stations. The theoretical capacity of the square in front of the station is calculated by using the important parameters of the model calibrated by the measured data under different lengths of the square in front of the station and the configuration of toll lanes. Then, the simulation platform of VISSIM software is used, and many experi-ments are carried out with relevant measured data to verify the accuracy of the model in multiple scenarios. Finally, the simulation value of the capacity of the square in front of the station is obtained, and the error is calculated by using the simulation value and the calculation value. The results show that the error of the verification result is 5.19%, and the error is within the allowable range, which shows that the model is accurate and feasible. The theoretical capacity calculated by the capacity model of the square in front of the toll station established in this paper is com-pared with the actual capacity, which can be used as a standard to judge the congestion degree of the square in front of the toll station and further provide a theoretical reference for evacuation of congestion.