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The newly-emerged pathological conditions and increased rates of drug resistance necessitate application of the state-of-the-art technologies for accelerated discovery of the therapeutic candidates and obtaining comprehensive knowledge about their targets, action mechanisms, and interactions within the body including those between the receptors and drugs. Using the physics- and chemistry-based modern techniques for theranostic purposes, preparing smart carriers, local delivery of genes or drugs, and enhancing pharmaceutical bioavailability could be of great value against the hard-to-treat diseases and growing drug resistance. Besides the artificial intelligence- and quantum-based techniques, crystal engineering capable of designing new molecules with appropriate characteristics, improving the stability and bioavailability of poorly soluble drugs, and efficient carrier development could play a crucial role in manufacturing efficient pharmaceuticals and reducing the adverse events. In this context, identifying the structures and behaviors of crystals and predicting their characteristics are of great value. Electron diffraction by accelerated analysis of the chemicals and sensitivity to charge alterations, electromechanical tools for controlled delivery of therapeutics, mechatronics via fabrication of multi-functional smart products including the organ-on-chip devices for healthcare applications, and optomechatronics by overcoming the limitations of conventional biomedical techniques could address the unmet biomedical requirements and facilitate development of more effective theranostics with improved outcomes., Competing Interests: Declaration of Competing Interest Authors declare no conflicts of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications. [ABSTRACT FROM AUTHOR]

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm's resilience under varying temperatures. [ABSTRACT FROM AUTHOR]

Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the "Agile eye" which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence (AI) technology and the Internet of Things (IoT) platform in recent years has allowed image detection systems to efficiently and gradually process data. The following study will be done in this paper to examine the image detection of AI based on mechatronics IoT technology. It is necessary to first describe the substance of mechatronics, the IoT, and other technologies, incorporate them into the IoT, and then examine and discuss the IoT's application potential. The second stage explains the fundamental ideas and various digital image detection algorithms that are really used in processing, and on the basis of these ideas, it suggests a number of automatic recognition processing approaches. In the third stage, different photos are classified using the Support Vector Machine (SVM) classifier based on AI and neural network technology, and the feature vectors of the target area are obtained through analysis and calculation. These are the conclusions: (1) The fusion of mechatronics and IoT technology has helped the AI industry innovate and grow to some level, which is helpful for fulfilling the fundamental criteria of an environment for intelligent picture recognition and detection. (2) An intelligent simulation model for image detection with recognition accuracy of over 95% and features of high speed, high precision, and high automation has raised the bar for detection and intelligent machine vision technology in China. (3) When SVM and neural networks are tested, the absolute error of the classification target value is very low, allowing them to meet the requirements for detection accuracy following error processing and a recognition rate of 90%. [ABSTRACT FROM AUTHOR]

Waste management is an essential component of urban management. As a waste solution, waste management is critical. The goal of this research is to develop a waste management database that is coupled with a mechatronic robot system. Compiling and gathering data on the sorts of garbage found in Indonesia is the starting point for this research. Indonesian waste is classified into six groups: cardboard, paper, metal, plastic, medical, and organic. The total images of the six groups are estimated at 1880 pictures. According to this picture database, Artificial Intelligence (AI) training was used to create the classification system. In the final AI process, the test method was performed using DenseNet121, DenseNet169, and DenseNet201. Testing using artificial intelligence DenseNet201 across 40 epochs yields the best 92,7% accuracy rate. Simultaneously with Artificial Intelligence testing, a mechatronic system is created as a direct implementation of the Artificial Intelligence output model. A four-servo arm robot with dc motor wheel mobility is included in the mechatronic system. According to these findings, the Indonesian waste database can be categorized correctly using Artificial Intelligence and the mechatronics system. This higher accuracy of the artificial intelligence model may be used to create a waste-sorting robot prototype. [ABSTRACT FROM AUTHOR]

Object recognition and tracking is a challenge for underwater vehicles. Traditional algorithm requires a clear feature definition, which suffers from uncertainty as the variation of occlusion, illumination, season and viewpoints. A deep learning approach requires a large amount of training data, which suffers from the computation. The proposed method is to avoid the above drawbacks. The Siamese Region Proposal Network tracking algorithm using two weights sharing is applied to track the target in motion. The key point to overcome is the one-shot detection task when the object is unidentified. Various complex and uncertain environment scenarios are applied to evaluate the proposed system via the deep learning model's predictions metrics (accuracy, precision, recall, P-R curve, F1 score). The tracking rate based on Siamese Region Proposal Network Algorithm is up to 180 FPS. [ABSTRACT FROM AUTHOR]

In this article mechatronic controller of brake pipe pressure was presented being the result of research and development work carried out at the Center of Rail Vehicles in Poznań. This function is performed by the module using binary voltage signals. Its structural structure and functionality are described and the results of empirical research are presented. The analysis of the research results indicates that the developed module can be used in the control systems of the railway air brake. [ABSTRACT FROM AUTHOR]

In this paper, a unique overview of intelligent machines and mathematical methods designed and developed to measure and to control the water level in industrial or laboratory setups of coupled and cascaded configurations of tanks is made. A systematized and concise overview is made of the mechatronic systems used in the measurement, identification, and control of the water level enumerates, the software used in the associated scientific research, modern techniques and sensors, and mathematical models, as well as analysis and control strategies. The broad overview of applications of the last decade is finalized by a proposition of a control system that is based on a parameter estimation of a new experimental setup, an integral dynamic model of the system, a modern mechatronic machine such as the Watson-Marlow peristaltic pump, the Anderson Negele sensor of level, the NI cRIO-9074 controller, and LabVIEW virtual instrumentation. The results of real experimental tests, exploiting a hybrid proportional control, being improved by a numerically predicted water level, are obtained using a few tools, i.e., the static characteristics, the classical step response, and a new pyramid-shaped step function of a discontinuous path-following reference input, being introduced to evaluate the effectiveness and robustness of the regulation of the level height. [ABSTRACT FROM AUTHOR]

In the era of internet and robots, sophisticated devices and real‐time simulation, the construction sector remains highly dependent on manual labor which is also the main source of delays, lack of quality, and human injuries. The rapid development in technological areas such as mechatronics, computers, sensors, programming, human interfaces, and artificial intelligence offers huge potential for modern applications. Nowadays, additive manufacturing is rapidly growing, and it could provide efficient solutions for steel construction since it can be executed in any shape and scale. There are a few solutions in additive manufacturing technologies, which can process materials and produce components. However, there is a gap in the market for capacity to link the processed material conditions and characteristics to the production parameters in real‐time. Real‐time and cognitive re‐planning of the process parameters becomes significantly important when components with large‐scale and complex shapes are considered. In steel construction, material should comply with strict requirements in terms of its strength and ductility. Hence, it is necessary to characterize the material properties before their application in construction. This paper presents material characterization for WAAM fabricated steel coupons, considering different directions with respect to the deposition direction. [ABSTRACT FROM AUTHOR]

Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined. [ABSTRACT FROM AUTHOR]

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective. [ABSTRACT FROM AUTHOR]

Both Model‐Based Systems Engineering (MBSE) and Artificial Intelligence (AI) have been challenged for their deployment in real‐world applications. Although MBSE remains the focal point of any systems engineering activities, its adoption still faces significant hurdles to demonstrate its return on investment. Recently, AI has received intensive attention, and its applications made their way into our daily life products. From an industrial perspective, within the context of the design and development of mechatronic systems, there is a lack of coherent foundation to enable the application of AI in MBSE. This vision paper discusses the role of AI in solving a set of MBSE challenges. As a result, we contribute by describing the actual MBSE adoption challenges and follow up with the characterization of the capabilities of AI in solving these challenges. With this initial work, we aim to trigger both AI and MBSE communities for further research discussions and industrial applications to help in achieving an intelligent design and development environment. [ABSTRACT FROM AUTHOR]

Automotive engineering is an area of great value and development. Lately, it has evolved rapidly because of autonomous vehicles. The development of smart mobility will be crucial in the coming years. Related research and companies related to intelligent transportation require trained and capable engineers. It is essential to generate an updated and specialized academic program that provides state-of-the-art technologies and related areas with smart mobility. This paper presents a novel two-year graduate academic program focused on smart electromobility. Programs around the globe were analyzed to find opportunity areas related to autonomous and electric vehicles, and smart mobility. Multi- and transdisciplinary courses were designed, according to the findings, on areas related to computer science, mechanical and electric engineering, law, marketing, and public policy. The proposed program fulfills the needs of a graduate student who will later work in a smart electromobility environment. The program offers a balanced curriculum that includes technical, business and social courses. Virtual and physical labs are proposed to develop a high-quality educational experience. This proposal can be used as a model for upcoming and related programs in other universities. [ABSTRACT FROM AUTHOR]

The article announces a special issue of the journal Advanced Robotics on Control, Mechatronics, and Integrated Design for Physical Human Support. The issue focuses on the integration of hardware and software to support humans in various situations, connecting recent progress in artificial intelligence technologies to motion control and mechatronics. The issue includes three original papers on topics such as vision-based path planning and obstacle avoidance in mobile robot applications, inverse kinematic modeling of soft manipulators, and the difference between bilateral and unilateral motor imagery. The authors express their gratitude to the contributors and reviewers of the special issue. [Extracted from the article]

Most sectors of the Nigerian economy, including the energy sector, have been infiltrated by digital technologies, including artificial intelligence (AI), where they have been extensively investigated. The research aims primarily to assess the perception of AI in facility management and identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analyzed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there is a high awareness level about AI in the energy sector. Also, awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had a high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to adopting AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, a scarcity of qualified experts and data scientists, and a growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel and adequate power system infrastructure to provide a stable power supply. [ABSTRACT FROM AUTHOR]

The digital revolution is changing the world. Robots, big data and artificial intelligence are the key technologies of the future and the basis of important innovations for the future development of the economy and society. In companies, this fact requires strategic rethinking and adjustments in ever-shorter time cycles. The creation of an agile and collaborative production to achieve the goals is often a basic requirement. With adaptation to technical progress, requirements and goals change continuously. To be and remain competitive, companies are forced to have at least the same technological standard as their competitors. In order to meet these challenges today, the use of highly efficient mechatronic systems such as robots is necessary. The paper analyses business ethics relevant aspects of robotics by using a survey with 88 respondents. [ABSTRACT FROM AUTHOR]

The article presents the possibilities of using artificial intelligence methods to model the injection doses of a modern Common Rail (CR) fuel injector. The presented neural network solution belongs to the experimental models known as black boxes in mechatronics. The backpropagation algorithm and its Levenberg-Marquardt expansion were used for the simulation. The analysis showed that there is a good match between the measurements and the computational model. The proposed solution can be used in the processes of diagnosing not only elements of the injection equipment, but also the internal combustion engine. [ABSTRACT FROM AUTHOR]

The domestic engineering industry is going through the stage of system introduction of digital technologies. The purpose of article is to present an experience of the department in the formation of the competences of a future engineer demanded in digital mechanical engineering. The department implements training methods corresponding to basic elements of a national technological initiative matrix such as digital modeling, mechatronics, additive technologies, artificial intelligence in control systems. Students master digital modeling in the end-to-end course of 3D modeling when dealing with design tasks of increasing complexity requiring the synthesis of independent, creative and competitive activities of students. Mechatronic modules are studied and designed in the educational process as the engineering competences demanded by employers. Additive technologies are studied at a laboratory of fast prototyping; the method of training combines team creation of electronic models, receiving a prototype of a product, and presentation of a result at student’s professionally focused competitions. Systems of artificial intelligence define the content of research work in interaction “student – teacher”. The new types of career-guidance work such as popular scientific lectures, excursions and practical work on the hi-tech equipment are highlighted. The authors also dwell on the prospects of training a new generation of mechanics in the conditions of the coming digital economy: the introduction of high-tech teaching aids, the implementation of the experience of immersive environments, gamification of training, the development of competences providing comprehensive skills for servicing virtual production corporations and intelligent devices.

A single-time velocity estimator-based reinforcement learning (RL) algorithm, integrated with a chaotic metaheuristic optimization technique is proposed in this article for the optimal path-planning of the nonholonomic robots considering a moving/stationary obstacle avoidance strategy. The additional contribution of the present study is by employing the Terramechanics principles to incorporate the effects of wheel sinkage into the deformable terrain on the dynamics of the robot aiming to find the optimal compensating force/torque magnitude to sustain a robust and smooth motion. The designed systematic control-oriented system incorporates a cost function of weighted components associated with the target-tracking and the obstacle avoidance. The designed velocity estimator contributes to the finite-state Markov decision process (MDP) in order to train the transition probabilities of the problem objectives. Based on the obtained results, the optimal solution for the Q-learning in terms of the adjusting factor for the minimized tracking error and obstacle collision risk propagation profiles is found at 0.22. The results further confirm the promising capacity of the proposed optimization-based RL algorithm for the collision avoidance control of the nonholonomic robots on deformable terrains.

Automotive engineering is an area of great value and development. Lately, it has evolved rapidly because of autonomous vehicles. The development of smart mobility will be crucial in the coming years. Related research and companies related to intelligent transportation require trained and capable engineers. It is essential to generate an updated and specialized academic program that provides state-of-the-art technologies and related areas with smart mobility. This paper presents a novel two-year graduate academic program focused on smart electromobility. Programs around the globe were analyzed to find opportunity areas related to autonomous and electric vehicles, and smart mobility. Multi- and transdisciplinary courses were designed, according to the findings, on areas related to computer science, mechanical and electric engineering, law, marketing, and public policy. The proposed program fulfills the needs of a graduate student who will later work in a smart electromobility environment. The program offers a balanced curriculum that includes technical, business and social courses. Virtual and physical labs are proposed to develop a high-quality educational experience. This proposal can be used as a model for upcoming and related programs in other universities.

Automated technological devices need diagnostics. At present, it is possible and necessary to use a wide range of sensors, computing systems and artificial intelligence methods based on neural networks, fuzzy inference systems in diagnostic systems. This paper proposes three models of information processes for diagnosing mechatronic systems - parallel, sequential and combined. To diagnose the nodes of mechatronic systems showed that it is necessary to combine neural networks and fuzzy logic. For a fuzzy inference system, it is proposed to use such parameters as the value of the diagnostic parameter, the trend of this parameter, and the value of the node operating time. Examples of diagnostics of such units of mechatronic systems as bearings and electric drives based on a neural network and fuzzy logic are given. Experiments have been carried out confirming the correctness of the reliability of the diagnostic model. [ABSTRACT FROM AUTHOR]

Teleoperation provides a promising way for human–robot collaboration in the unknown or unstructured environments to perform a cooperative task. It enables humans to complete a task at a remote side and combines both the human's intelligence and the robots’ capabilities in a collaborative task. Therefore, it is necessary to conduct cross researches in terms of robotics, artificial intelligence, sensors, and mechatronics. This study covers the state-of-the-arts research in terms of perception, control, and learning. In this review, key technologies about information perception mechanism, control algorithms, human–robot interface, and robot learning strategies for teleoperation are introduced. Then, a comprehensive survey is summarised in representative applications in teleoperation, and the existing challenges and potential directions of the development are discussed.

Due to the integration of complex structure and artificial intelligence, the failure behavior of intelligent mechatronic system (IMS) is characterized by functional and physical dependences. In this article, the functional isolation and physical isolation (PI) effects caused by these dependences are studied. We not only analyze the PI effect affecting the failure behaviors of components through failure mechanisms, but also propose a hierarchical method based on binary decision diagram for modeling system failure behavior affected by the PI and functional isolation effect. In the case study, an automatic collision avoidance system as an example of IMS is evaluated for the analysis of failure behavior, which is helpful for more realistic reliability assessment and design optimization. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence has promoted the rapid development of intelligence in various fields, with mechatronics being one of its hot research topics. It is a common trend to combine the development of mechatronic systems with artificial intelligence, which is mainly designed and realized through control technology; that is, it is realized using the control system in mechatronic systems. The last paper about an electrohydraulic control system was authored by G. Zhao, S. Chen, Y. Liu, and K. Guo, who analyzed the pressure control problem in a dual-actuator-driven electrohydraulic actuator [[3]]. [Extracted from the article]

Result of the Scientific Concerns from the Doctoral School of Mechanical Engineering and Mechatronics of the Valahia Târgovişte University and the research project of INCDMTM "INTEGRATED MECHATRONIC SYSTEM FOR HUMAN SECURITY INSURANCE FOR THE SAFETY OF OBJECTIVES AND INTERVENTIONS IN RISK - MISO ZONES" (project ID: PED-2016- 0924, code PN-III-P2-2.1-PED-2016-0707) in the field of robotics, the scientific work "Intelligent Platform with BLDC Drives and Microsystems for Mechatronic Applications in Security and Surveillance " is the completion of the experimental testing of controlling the movement of a security and surveillance robot, as part of the Ph.D. industrial thesis "Studies, research and contributions on the development of a smart mecatronic robot for security and surveillance applications". The scientific work ultimately results in an intelligent, original platform that will be used to control the movement of the robot. The platform allows communication between the latest generation BLDC engine (embedded in the drive wheel) and it's controller and a computerized microsystem that will handle the displacement controls and will also provide the link with the human operator through any remote guidance system that is used. Although designed for an intelligent security and surveillance mechatronic robot, this platform is proven to be extensively versatile for any other type of robot or mobile platform that uses BLDC wheel-drive engines. The project harmoniously combines Mechatronics, Cyber-MixMeatronic, Integronics and Artificial Intelligence into an Intelligent Interoperable Construction. [ABSTRACT FROM AUTHOR]