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Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration of new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables modeling and research into ways to achieve better sustainability, greater efficiency, and improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, and potential research directions to provide the infrastructure and underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along these lines allows for a better consideration of their individual risk factors and use of available data, resulting in an approach to generate holistic virtual representations (DTs) to facilitate predictive analyses in industrial practices. Artificial intelligence-based DTs are becoming a new tool for monitoring, simulating, and optimizing systems, and the widespread implementation and mastery of this technology will lead to significant improvements in performance, reliability, and profitability. Despite advances, the aforementioned technology still requires research, improvement, and investment. This article's contribution is a concept that, if adopted instead of the traditional approach, can become standard practice rather than an advanced operation and can accelerate this development. [ABSTRACT FROM AUTHOR]

Three-dimensional printing has become a fast-growing industry. The first phase of this technology is the design of a 3D object to personalize it and optimize its production. This paper explores the procedural generation of the 3D model. The article aims to present the method of procedurally generating 3D objects in Python. Procedural content generation is the automated creation of content using algorithms. Most often, as part of procedural generation, a small number of input parameters and pseudo-random processes are used to generate content that will meet the requirements. The programming techniques for object customization in Python optimize the manufacturing process. Moreover, procedural generation speeds up the model design, and if developers use 3D scanning methods and artificial intelligence, production can be personalized, which is in line with the concept of Industry 4.0. [ABSTRACT FROM AUTHOR]

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI's ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper's contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors' knowledge and experience in renewable energy, AI, and mechatronics. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the work include new artificial intelligence-based software for optimizing 3D printing and products and services realized through additive manufacturing. Rapidly developing artificial intelligence (AI) can help machines and devices to perceive, analyze, and even make inferences in a similar way to human reasoning. The aim of this article is to present applications of AI methods, including machine learning (ML), in the design and supervision of processes used in the field of additive manufacturing techniques. This approach will allow specific tasks to be solved as if they were performed by a human expert in the field. The application of AI in the development of additive manufacturing technologies makes it possible to be assisted by the knowledge of experienced operators in the design and supervision of processes acquired automatically. This reduces the risk of human error and simplifies and automates the production of products and parts. AI in 3D technology creates a wide range of possibilities for generating 3D objects and enables a machine equipped with a vision system, used in ML processes, to analyze data similar to human thought processes. Incremental printing using such a printer allows the production of objects of ever-increasing quality from several materials simultaneously. The process itself is also precise and fast. An accuracy of 97.56% means that the model is precise and makes very few errors. The 3D printing system with artificial intelligence allows the device to adapt to, for example, different material properties, as the printer examines the 3D-printed surface and automatically adjusts the printing. AI/ML-based solutions similar to ours, once learning sets are modified or extended, are easily adaptable to other technologies, materials, or multi-material 3D printing. They also allow the creation of dedicated, ML solutions that adapt to the specifics of a production line, including as self-learning solutions as production progresses. [ABSTRACT FROM AUTHOR]

This article presents the current state and development directions of the tire industry. One of the main requirements that a tire must meet before it can leave the factory is achieving values of quantities describing uniformity at a defined level. Of particular importance areconicity and the components of the tire with the greatest impact on its value. This research is based on the possibility of using an ANN to meet contemporary challenges faced by tire manufacturers. In order to achieve a satisfactory level of prediction, we compared the use of a multi-layer perceptron and decision trees XGBoost, LightGbmRegression, and FastTreeRegression. Based on data analysis and similar examples from the literature, metrics were selected to evaluate the models' ability to solve regression problems in relation to the described problem. We selected the best possible solution, standing at the top of the features covered by the criterion analysis. The proposed solutions can be the basis for acquiring new knowledge and contributions in the field of the computational analysis of industrial data in tire production. These solutions are characterized by the required accuracy and efficiency for online work, and they also contribute to the creation of the best fit elements of complex systems (including computational models). The results of this study will contribute to reducing the volume of waste in the tire industry by eliminating defective tire parts in the early stages of the production process. [ABSTRACT FROM AUTHOR]

The pace of technological development, including smart factories within Industry 4.0/5.0, means that the vagaries of supply chains observed previously cannot be repeated. The automation and computerization of supply chains, asset tracking, simulation, and the prediction of disruption through artificial intelligence (AI) are becoming a matter of course. In selected countries, this will be facilitated by sixth-generation mobile networks planned for full deployment in 2030. The 6G-based intelligent supply chain management within the Industry 4.0/5.0 paradigm will ensure not only greater fluidity of supply, but also faster response to changes in market availability or prices, allowing substitutes to be found and taken into account in the production process and its logistical provisioning. The article outlines key research and development trends in this area and identifies priority development directions, taking into account the advantages and opportunities offered by the Industrial Internet of Things (IIoT) and machine learning (ML). The emergence of 6G technology will transform the supply chain with unprecedented speed, connectivity, and efficiency. This technology will improve visibility, automation, and collaboration while supporting sustainable and safe operations. As a result, companies will be able to design, plan, and operate their supply chains with greater precision, flexibility, and responsiveness, ultimately leading to a more robust and agile supply chain ecosystem. [ABSTRACT FROM AUTHOR]

Supporting the decision-making process for the production of a 3D-printed hand exoskeleton within the Industry 4.0 and Industry 5.0 paradigms brings new concepts of manufacturing procedures for 3D-printed medical devices, including hand exoskeletons for clinical applications. The article focuses on current developments in the design and manufacturing of hand exoskeletons and their future directions from the point of view of implementation within the Industry 4.0 and Industry 5.0 paradigms and applications in practice. Despite numerous publications on the subject of hand exoskeletons, many have not yet entered production and clinical application. The results of research on hand exoskeletons to date indicate that they achieve good therapeutic effects not only in terms of motor control, but also in a broader context: ensuring independence and preventing secondary motor changes. This makes interdisciplinary research on hand exoskeletons a key study influencing the future lives of patients with hand function deficits and the further work of physiotherapists. The main aim of this article is to check in what direction hand exoskeletons can be developed from a modern economic perspective and how decision support systems can accelerate these processes based on a literature review, expert opinions, and a case study. [ABSTRACT FROM AUTHOR]

The sixth generation of mobile networks (6G) has the potential to revolutionize the way we communicate, interact, and use information for machine-to-machine (M2M) communication in Industry 4.0 and Industry 5.0, while also improving coverage in places that were previously considered difficult to access and/or digitally excluded, and supporting more devices and users. The 6G network will have an impact through a combination of many technologies: the Internet of Things (IoT), artificial intelligence/machine learning, virtual and augmented reality, cloud computing, and cyber security. New solutions and architectures and concepts for their use need to be developed to take full advantage of this. This article provides an overview of the challenges in this area and the proposed solutions, taking into account the disruptive technologies that are yet to be developed. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests. [ABSTRACT FROM AUTHOR]

The future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before moving on to more effective, but also more invasive, forms of therapy. The main motivation of our study was to improve existing and develop new AI-based solutions for cardiac preventive medicine, with particular emphasis on the prevention of heart attacks. This is due to the fact that the epidemic of lifestyle diseases (including cardiologic ones) has been stopped but not reversed; hence, automatically supervised prevention using AI seems to be a key opportunity to introduce progress in the above-mentioned areas. This can have major effects not only scientific and clinical in nature, but also economic and social. The aim of this article is to develop and test an AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine. It used the combination and comparison of multiple AI methods and techniques to determine a personalized heart attack probability based on a wide range of patient characteristics and, from a computational point of view, determine the minimum set of characteristics necessary to do so. When applied to a specific patient, this represents progress in this field of research, resulting in improvements in preclinical care and diagnostics, as well as predictive accuracy in preventive medicine. After an initial selection based on the authors' knowledge and experience, four solutions turned out to be the best: linear support vector machine (Linear SVC), logistic regression, k-nearest neighbors algorithm (KNN, k-NN), and random forest. A comparison of the models developed in the study shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis—selecting patients who require further, more accurate testing. In addition, this can be performed based on a reduced set of parameters, particularly heart rate, age, BMI, and cholesterol. This allows the development of a prevention strategy based on modifiable factors (e.g., in the form of diet, activity modification, or a hybrid combining different factors) combined with the monitoring of heart attack risk by the proposed system. The novelty and contribution of the described system lies in the use of AI for a widely available, cheap, and quick predictive analysis of cardiovascular functions in a group of patients classified as at risk, and over time in all patients as a standard periodic examination qualifying them for further, more advanced diagnosis of heart diseases. [ABSTRACT FROM AUTHOR]

The aim of the study was to develop a system supporting technological process planning, the functioning of which would resemble the way human experts act in their fields of expertise, one capable of gathering necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods, and their significant effectiveness in supporting technological process planning. Technological-process planning based on an expert system is divided into the following stages: the selection of the semi-finished products; the establishing of the technological process structure, and the selection of the workpiece instrumentation, machine tools, tools, and tooling and machining parameters for each technological operation. The system-embedded knowledge takes the form of neural networks, decision trees and facts. The system is presented using the example of a real enterprise. The intelligent expert system is dedicated to process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise, and are not very familiar with its machinery and other means of production.

The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to cleaner and more sustainable energy sources. AI is playing an increasingly important role in transforming energy markets in various aspects of the industry in different ways, including smart grids and energy management, renewable energy integration, energy forecasting and trading, demand response and load management, energy efficiency and conservation, maintenance and asset management, energy storage optimization, carbon emission reduction, market analytics and risk management, exploration and production, regulatory compliance, and safety. The aim of this article is to discuss our own AI-based computational model in sustainable transformation of energy markets and to lay the foundations for further harmonious development based on a computational (AI/ML-based) models, with particular reference to current limitations and priority directions for further research. Such an approach may encourage new research for the practical application of AI algorithms in critical domains of the energy sector. [ABSTRACT FROM AUTHOR]

Machine learning is increasingly being used to solve clinical problems in diagnosis, therapy and care. Aim: the main aim of the study was to investigate how the selected machine learning algorithms deal with the problem of determining a virtual mental health index. Material and Methods: a number of machine learning models based on Stochastic Dual Coordinate Ascent, limited-memory Broyden–Fletcher–Goldfarb–Shanno, Online Gradient Descent, etc., were built based on a clinical dataset and compared based on criteria in the form of learning time, running time during use and regression accuracy. Results: the algorithm with the highest accuracy was Stochastic Dual Coordinate Ascent, but although its performance was high, it had significantly longer training and prediction times. The fastest algorithm looking at learning and prediction time, but slightly less accurate, was the limited-memory Broyden–Fletcher–Goldfarb–Shanno. The same data set was also analyzed automatically using ML.NET. Findings from the study can be used to build larger systems that automate early mental health diagnosis and help differentiate the use of individual algorithms depending on the purpose of the system. [ABSTRACT FROM AUTHOR]

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient. [ABSTRACT FROM AUTHOR]

If we look at the chronology of transitions between successive stages of industrialization, it is impossible not to notice a significant acceleration. There were 100 years between the industrial revolutions from 2.0 to 3.0, and only half a century passed from the conventional 3.0 to 4.0. Assuming that progress will inevitably continue to accelerate, and given that 2011 is the set date for the start of the fourth industrial revolution, we can expect Industry 5.0 by 2035. In recent years, Industrial Internet of Things (IIoT) applications proliferated, which include multiple network elements connected by wired and wireless communication technologies, as well as sensors and actuators placed in strategic locations. The significant pace of development of the industry of advantages in predicting threats to infrastructure will be related to the speed of analyzing the huge amount of data on threats collected not locally, but globally. This article sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), to significantly impact IIoT cyber threat prediction in Industry 5.0. [ABSTRACT FROM AUTHOR]

Featured Application: The potential application of the work includes conceptual work on the use of artificial intelligence in technical control and maintaining the quality of tires during operation. The tire industry plays a key role in ensuring safe and efficient transportation. With 1.1 billion vehicles worldwide relying on tires for optimum performance, tire quality control is of paramount importance. In recent years, the integration of artificial intelligence (AI) has revolutionized various industries, and the tire industry is no exception. In this article, we take a look at the current state of quality control in the tire industry and the transformative impact of AI on this crucial process. Automatic detection of tire defects remains an important and challenging scientific and technical problem in industrial tire quality control. The integration of artificial intelligence into tire quality control has the potential to transform the tire industry, leading to safer, more reliable, and more sustainable tires. Thanks to continuous progress and a proactive approach to challenges, the tire industry is prepared for a future in which artificial intelligence will play a key role in delivering high-quality tires to consumers around the world. [ABSTRACT FROM AUTHOR]

Featured Application: Specific application or a potential application of the work includes novel solutions in exoskeletons technology. An aging population, the effects of pandemics and civilization-related conditions, and limited leapfrogging in the number of rehabilitation and physiotherapy specialists are driving demand for modern assistive technologies, especially upper and lower limb exoskeletons. Patient-tailored devices are a rapidly developing group of technologies, both from a biomechanics, informatics, and materials engineering perspective. In particular, the technological development of 3D printing, the expanding range of available materials and their properties (including contact with living tissue and bodily fluids), and the possibility of selecting and optimizing them using artificial intelligence (including machine learning) are encouraging the emergence of new concepts, particularly within the Industry 4.0 paradigm. The article provides an overview of what is available in this area, including an assessment of as yet untapped research and industrial and, in part, clinical potential. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this work relates to the use of 3D printing and artificial intelligence as systems to support the personalisation of medical devices, in particular powered upper limb exoskeletons and passive orthoses. The human hand is the most precise and versatile tool that nature has given man, and any deficits in this area affect the functional capabilities and quality of human life. Scientists, engineers and clinicians are constantly looking for solutions in the field of diagnosis, treatment, rehabilitation and care of patients with hand function deficits. One such solution is a hand exoskeleton. In the process of designing and testing the hand exoskeleton, emphasis should be placed on the full usability and comfort of the system; hence, the issues of personalization, matching and testing are crucial for the development of the discussed group of solutions. The aim of this paper is to present the possibilities of personalizing 3D-printed medical devicesbased on our own experience in functional user assessment andthe material selection, design, optimization using artificial intelligence and production and testing of several generations of different upper limb exoskeletons, incorporatingthe considerations of the Medical Device Regulation (MDR), ISO 13485 and ISO 10993 standards.The novelty and possible contribution of the proposed approach consist of the possibilities and limitations of the personalization of the upper limb exoskeleton discussed in the article as well as the directions of further development of significant scientific, technical and clinical importance. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the work include the computerization of production processes and the construction of artificial intelligence-based systems to support and optimize tool selection on existing and newly designed assembly lines in line with Industry 4.0 and Internet of Things paradigms. Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists' extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Properly developed technology affects the entire production process. This paper presents an attempt to develop a post-hoc model of already existing manufacturing processes with the increased requirements and expectations resulting from the introduction of the Industry 4.0 paradigm. In particular, we relied on fuzzy logic to support the description of uncertainties, incomplete data, and discontinuities in the manufacturing process. This translates into better controls compared to conventional systems. An analysis of the proposed solution's limitations and proposals for further development constitute the novelty and contribution of the article. [ABSTRACT FROM AUTHOR]

Cyber security is nowadays synonymous with the reliability of elements connected to the internet. Better control of factories, security systems or even individual sensors is possible through the use of Internet of Things technology. The security of the aforementioned structures and the data they transmit has been a major concern in the development of IoT solutions for wireless data transmission. If we add to this prospect of low-cost end devices, we can seriously consider implementing such solutions in critical infrastructure areas. This article aims to assess the state of the art and experience and identify the main risks and directions for further development in order to improve the cyber security situation of LoRaWAN-based networks. LoRaWAN meets the three key requirements of IoT applications (low cost, large-scale deployability, high energy efficiency) through an open standard and the construction of autonomous networks without third-party infrastructure. However, many research issues remain to be solved/improved such as resource allocation, link coordination, transmission reliability, performance and, above all, security. Thus, we have defined a research gap in the area of LoRaWAN security. The contribution of this work is to structure the knowledge in the field of LoRaWAN security, based on previous publications and our own experience, in order to identify challenges and their potential solutions. This will help move LoRaWAN security research to the next stage. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the findings presented in this article include low-cost, fast, and simple gait analysis systems for use by primary care physicians, orthopedists, neurologists, and geriatricians, enabling the screening of patients who require a further more accurate gait diagnosis, including as part of preventive medicine. Research on gait function assessment is important not only in terms of the patient's mobility, but also in terms of the patient's current and future quality of life, ability to achieve health goals, family life, study and/or work, and participation in society. The main methods used herein include a literature review and an analysis of our own original research and concepts. This study used the historical data of 92 ischemic stroke patients (convenience trial) undergoing two kinds of rehabilitation. An artificial neural network, fractal analysis, and fuzzy analysis were used to analyze the results. Our findings suggest that artificial neural networks, fuzzy logic, and multifractal analysis are useful for building simple, low-cost, and efficient computational tools for gait analysis, especially in post-stroke patients. The novelty lies in the simultaneous application of the three aforementioned technologies to develop a computational model for the analysis of a patient's post-stroke gait. The contribution of this work consists not only in its proposal of a new and useful clinical tool for gait assessment, even in the most severe post-stroke cases, but also in its attempt to offer a comprehensive computational explanation of observed gait phenomena and mechanisms. We conclude by anticipating more advanced and broader future applications of artificial intelligence (AI) in gait analysis, especially in post-stroke patients. [ABSTRACT FROM AUTHOR]

Featured Application: The results of this work can be used to support future choices regarding machine learning algorithms in various application domains. It is increasingly important to provide post-stroke patients with rapid access to patient-tailored assistive technologies to increase independence, mobility, and participation. Automating the selection of assistive devices based on artificial intelligence could speed up the process and improve accuracy. It would also relieve the burden on diagnosticians and therapists and speed up the introduction of new ranges by automating databases. This article compares selected machine learning classification methods in the area of post-stroke rehabilitation device selection. The article covers the specifics of the selection, the choice of classification methods, and the identification of the best one, as well as the experimental part, the description of the results, the comparison process, and directions for further research. The novelty lies both in the topic, as the choice of classification method has an impact on the accuracy of classification in the selection of medical materials, and in the manner of the comprehensive approach. The possible contribution is of great scientific and clinical relevance, but above all, it has economic and social importance, enabling post-stroke individuals to return more quickly to the community, learning, and work, and relieving the burden on the health care system. [ABSTRACT FROM AUTHOR]

Featured Application: Semi-automatic or automatic systems supporting rehabilitation of laryngectomised patients by improving the quality of oesophageal speech. This paper presents an approach to extraction techniques for speaker recognition following total laryngectomy surgery. The aim of the research was to develop a pattern of physical features describing the oesophageal speech in people after experiencing laryngeal cancer. Research results may support the speech rehabilitation of laryngectomised patients by improving the quality of oesophageal speech. The main goal of the research was to isolate the physical features of oesophageal speech and to compare their values with the descriptors of physiological speech. Words (in Polish) used during speech rehabilitation were analyzed. Each of these words was divided into phonetic segments from which the physical features of speech were extracted. The values of the acquired speech descriptors were then used to create a vector of the physical features of oesophageal speech. A set of these features will determine a model that should allow us to recognize whether the speech-rehabilitation process is proceeding correctly and also provide a selection of bespoke procedures that we could introduce to each patient. This research is a continuation of the analysis of oesophageal speech published previously. This time, the effectiveness of parameterization was tested using methodologies for analyzing the phonetic segments of each word. [ABSTRACT FROM AUTHOR]

Featured Application: Maintaining production systems within Industry 4.0 facilitates the application of artificial intelligence methods, techniques and tools to predict potential failures and to take maintenance actions in advance at the right time and in the right way to avoid or minimize their harmful impact, including using digital twins. Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this work concerns the use of 3D printing and AI as systems supporting the design and manufacture of medical devices, especially powered exoskeletons and passive orthoses. Three-dimensional-printed medical devices are a separate group of medical devices necessary for the development of personalized medicine. The present article discusses a modern and specific group of medical devices and exoskeletons, which aims to present our own experiences in the selection of materials, design, artificial-intelligence optimization, production, and testing of several generations of various upper limb exoskeletons when considering the Medical Devices Regulation (MDR) and the ISO 13485 and ISO 10993 standards. Work is underway to maintain the methodological rigor inherent in medical devices and to develop new business models to achieve cost-effectiveness so that inadequate legislation does not stop the development of this group of technologies (3D scanning, 3D printing, and reverse engineering) in the healthcare system. The gap between research and engineering practice and clinical 3D printing should be bridged as quickly and as carefully as possible. This measure will ensure the transfer of proven solutions into clinical practice. The growing maturity of 3D printing technology will increasingly impact everyday clinical practice, so it is necessary to prepare medical specialists and strategic and organizational changes to realize the correct implementation based on the needs of patients and clinicians. [ABSTRACT FROM AUTHOR]

This paper attempts to conduct a comparative life cycle environmental analysis of alternative versions of a product that was manufactured with the use of additive technologies. The aim of the paper was to compare the environmental assessment of an additive-manufactured product using two approaches: a traditional one, based on the use of SimaPro software, and the authors’ own concept of a newly developed artificial intelligence (AI) based approach. The structure of the product was identical and the research experiments consisted in changing the materials used in additive manufacturing (from polylactic acid (PLA) to acrylonitrile butadiene styrene (ABS)). The effects of these changes on the environmental factors were observed and a direct comparison of the effects in the different factors was made. SimaPro software with implemented databases was used for the analysis. Missing information on the environmental impact of additive manufacturing of PLA and ABS parts was taken from the literature for the purpose of the study. The novelty of the work lies in the results of a developing concurrent approach based on AI. The results showed that the artificial intelligence approach can be an effective way to analyze life cycle assessment (LCA) even in such complex cases as a 3D printed medical exoskeleton. This approach, which is becoming increasingly useful as the complexity of manufactured products increases, will be developed in future studies. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of impact force on stopper screw in axial piston hydraulic motor. The solution contains probabilistic description of input variables. If the output parameters of probabilistic solution are compared with arbitrary values and values acquired by analytical solution, the probability of proper operation of the device can be evaluated.

Reviewing the current state of knowledge on sustainable production, this paper opens the Special Section entitled “Sustainability in production in the context of Industry 4.0”. The fourth industrial revolution (Industry 4.0), which embodies a vision for the future system of manufacturing (production), focuses on how to use contemporary methods (i.e. computerization, robotization, automation, new business models, etc.) to integrate all manufacturing industry systems to achieve sustainability. The idea was introduced in 2011 by the German government to promote automation in manufacturing. This paper shows the state of the art in the application of modern methods in sustainable manufacturing in the context of Industry 4.0. The authors review the past and current state of knowledge in this regard and describe the known limitations, directions for further research, and industrial applications of the most promising ideas and technologies. [ABSTRACT FROM AUTHOR]

Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this paper, we propose a transformation of the feature space and definition of class labels in the post-stroke gait problem to more efficiently study related phenomena and assess gait faster. Clustering is used to define two class labels (improvement and recurrence) in the data labeling process. The proposed approach was tested on a real-world dataset consisting of 50 patients (male and female, aged 49–82 years) after ischemic stroke who participated in a gait rehabilitation program. Gait in the study was described using speed, cadence, and stride length and their normalized values. Ten treatment sessions (10 therapy days) were conducted over two weeks (10 working days). The same specialist took measurements, and hence inter-rater reliability can be neglected. Machine learning methods, support vector machine and quadratic discriminant analysis were used to classify post-stroke gait for three cases with different class labels. The proposed novel approach, characterized by its speed of execution and accuracy of classification, may be helpful for screening, better targeting, and rehabilitation monitoring. The proposed approach minimizes clinical testing and supports the work of physicians, physiotherapists, and diagnosticians. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the concepts and solutions presented in this article relate to AI-based preventive medicine systems and second opinion systems. The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The aim of the project was to develop a relatively simple artificial-intelligence tool to assess the likelihood of a heart infarction for preventive medicine purposes. We used binary classification to determine from a wide variety of patient characteristics the likelihood of heart disease and, from a computational point of view, determine what the minimum set of characteristics permits. Factors with the highest positive influence were: cp, restecg and slope, whilst factors with the highest negative influence were sex, exang, oldpeak, ca, and thal. The novelty of the described system lies in the development of the AI for predictive analysis of cardiovascular function, and its future use in a specific patient is the beginning of a new phase in this field of research with a great opportunity to improve pre-clinical care and diagnosis, and accuracy of prediction in preventive medicine. [ABSTRACT FROM AUTHOR]

Featured Application: Potential application of the work concerns AI-supported eco-design of a novel family of products. Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of research. The article deals with issues relating to eco-design, eco-innovation, and inventory. The process of selecting inventive methods was presented. Selected inventive methods used to develop the product concept were briefly characterized. Creativity sessions were conducted using the methods of brainstorming, stimulating, reverse brainstorming, word games, and superpositions. The effect of these activities is the concept for an eco-innovative product. A product design was developed that is highly recyclable and environmentally friendly. An ecological analysis of the designed product, including AI-based (artificial neural networks), was carried out, which showed the legitimacy of the actions taken to develop an environmentally friendly product. The novelty of the proposed approach consists of combining the use of research data, with new methods for their analysis using both traditional and artificial intelligent tools, to create a transparent and scalable product design. To date, this approach is unique and has no equivalent in the literature. Despite higher manufacturing costs, the more environmentally friendly refrigerator is cheaper in operation (consumes less energy) due to the ecological solutions incorporated into its design. [ABSTRACT FROM AUTHOR]

Featured Application: The potential application of the research results presented in this paper relates to artificially intelligent (deep learning-based) techniques for the design and production of 3D printed chainmails with personalized mechanical properties, including for medical exoskeletons (wearable robots). The aim of this paper is to refine a scientific solution to the problem of automated or semi-automated efficient and practical design of 3D printed chainmails of exoskeletons with pre-programmed properties (variable stiffness/flexibility depending on direction) reflecting individual user needs, including different types and degrees of deficit. We demonstrate this with the example of using chainmail in a hand exoskeleton, where 3D printed chainmail components can be arranged in a single-layer structure with adjustable one- or two-way bending modulus. The novelty of the proposed approach consists in combining the use of real data from research on the exoskeleton of the hand, new methods of their analysis using deep neural networks, with a clear and scalable design of a 3D printed fabric product that can be personalized (mechanical parameters such as stiffness and bend angles in various directions) to the needs and goals of therapy in a particular patient. So far, this approach is unique, having no equivalent in the literature. This paves the way for a wider implementation of adaptive chainmails based on machine learning, more efficient for more complex chainmail designs. [ABSTRACT FROM AUTHOR]

Featured Application: A potential application of the work relates to automated or semi-automated systems for the design and production of chainmail with preset or personalised properties for medical applications. The problem of computerising the design and development of 3D-printed chainmail with programmed directional functions provides a basis for further research, including the automation of medical devices. The scope of the present research was focused on computational optimisation of the selection of materials and shapes for 3D printing, including the design of medical devices, which constitutes a significant scientific, technical, and clinical problem. The aim of this article was to solve the scientific problem of automated or semi-automated efficient and practical design of 3D-printed chainmail with programmed directional functions (variable stiffness/elasticity depending on the direction). We demonstrate for the first time that 3D-printed particles can be arranged into single-layer chainmail with a tunable one- or two-directional bending modulus for use in a medical hand exoskeleton. In the present work, we accomplished this in two ways: based on traditional programming and based on machine learning. This paper presents the novel results of our research, including 3D printouts, providing routes toward the wider implementation of adaptive chainmails. Our research resulted in an automated or semi-automated efficient and practical 3D printed chainmail design with programmed directional functions for a wrist exoskeleton with variable stiffness/flexibility, depending on the direction. We also compared two methodologies of planning and construction: the use of traditional software and machine-learning-based software, with the latter being more efficient for more complex chainmail designs. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting. [ABSTRACT FROM AUTHOR]

The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company's employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is changing many areas of technology in the public and private spheres, including the economy. This report reviews issues related to machine modelling and simulations concerning further development of mechanical devices and their control systems as part of novel projects under the Industry 4.0 paradigm. The challenges faced by the industry have generated novel technologies used in the construction of dynamic, intelligent, flexible and open applications, capable of working in real time environments. Thus, in an Industry 4.0 environment, the data generated by sensor networks requires AI/CI to apply close-to-real-time data analysis techniques. In this way industry can face both fresh opportunities and challenges, including predictive analysis using computer tools capable of detecting patterns in the data based on the same rules that can be used to formulate the prediction. [ABSTRACT FROM AUTHOR]

The application of modern methods (AI and CI) in the field of mechanical engineering is particularly interesting due to its research and practical character and very strong references to Industry 4.0 [4-6]. His main research interest is in knowledge discovery in clinical data sets, applications of artificial intelligence in biomedicine, computational models of brain processes, AI-based optimization of additive machining, and rehabilitation robots including exoskeletons. Machine modelling and simulation is the use of models (e.g. a physical, mathematical, or logical representation) as a framework for simulation to develop the data used to make technical decisions, in the field of mechanical engineering. The work by Drelich et al. confirms the usefulness of the applied ultrasonic method for testing macroscopic inhomogeneity of corrugated fibre-cement boards. [Extracted from the article]

The binary classifiers are appropriate for classification problems with two class labels. For multi-class problems, decomposition techniques, like one-vs-one strategy, are used because they allow the use of binary classifiers. The ensemble selection, on the other hand, is one of the most studied topics in multiple classifier systems because a selected subset of base classifiers may perform better than the whole set of base classifiers. Thus, we propose a novel concept of the dynamic ensemble selection based on values of the score function used in the one-vs-one decomposition scheme. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The proposed approach is compared with the static ensemble selection method based on the integration of base classifiers in geometric space, which also uses the one-vs-one decomposition scheme. In addition, other base classification algorithms are used to compare results in the conducted experiments. The obtained results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A "digital twin" is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. Using this type of solution has become an integral part of Industry 4.0, offering businesses tangible benefits, in addition to being particularly effective within the context of sustainable production and maintenance. The purpose of this paper is to present the results of research on the development of digital twins of technical objects, which involved data acquisition and their conversion into knowledge, the use of physical models to simulate tasks and processes, and the use of simulation models to improve the physical tasks and processes. In addition, monitoring processes and process parameters allow for the continued improvement of existing processes as regards intelligent eco-designing and planning and monitoring production processes while taking into account sustainable production and maintenance. [ABSTRACT FROM AUTHOR]

An exoskeleton is defined as a distinctive kind of robot to be worn as an overall or frame, effectively supporting, or in some cases substituting for, the user's own movements. In this paper a new three-dimensional (3D) printed bydgostian hand exoskeleton is introduced and biomedically characterized. The proposed concept is promising, and the described approach combining biomechanical factors and 3D modeling driven by detailed hand exoskeleton patterns may constitute a key future method of ergonomic hand exoskeleton design and validation prior to manufacturing. Despite the aforementioned approach, we should be aware that hand exoskeleton constitutes hand support and rehabilitation robot system developing with the user; thus, certain coordination and continuity of the "hardware" part of the whole system and the training paradigm are essential for therapy efficacy. [ABSTRACT FROM AUTHOR]

The central objective of the present author's research is to develop a system supporting the design of a technological process (a computer-aided process planning system) that functions similarly to a human expert in the field in question. The use of neural networks makes the creation of such a system possible. The proposed method uses a system of three blocks of neural networks, and involves the creation of neural networks to be used for the selection of machines, tools, and machining parameters. These networks are built for each process operation separately; that is, a set of neural networks is created for each selection. For the construction of models, different types of neural networks (multilayer networks with error backpropagation, radial basis function, and Kohonen) with different structures were employed, and the networks that made the best selections were identified. A method was also developed for the elimination of defects occurring during the production process. When a defect comes to light, this method suggests changes to the technological process, thus improving the quality of that process. Guidelines for the elimination of defects are produced in the form of decision rules. Such a computer-aided process planning system will be especially useful for process engineers who do not yet have sufficient experience in the design of technological processes, or who have only recently joined a particular manufacturing enterprise and are not fully familiar with its machines and other means of production (tools and instrumentation). It should be emphasized that such a system performs an advisory role, and it is always the process engineer who makes the final decision. The neural network models were tested on real data from an enterprise. A computer-aided process planning system based on rules and neural network models enables the intelligent design of technological processes. [ABSTRACT FROM PUBLISHER]

In the article the neural networks used for failures location for water supply networks are presented. To do this a hydraulic model of the water net, as well as an appropriate developed monitoring system have to be used. The current applications of monitoring systems installed in the waterworks do not realize their possibilities. The monitoring systems provided as autonomic programs to collect and record the information about flows and pressures of water in source pumping stations, in the pump stations bringing up the water pressure inside the water net and in the pipes of water supply network give a general knowledge about state of its work, but if they would be used as elements of IT systems supporting the water network management, they could help to solve the tasks concerning detection and localization of water leaks. The models of failures location in water nets described in the paper are created by means of neural networks in the form of MLP nets. [ABSTRACT FROM AUTHOR]