rss_2.0Sciendo RSS Feed for Journal of Automation, Mobile Robotics and Intelligent Systemshttps://sciendo.com/journal/JAMRIShttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/66e3383b7d402026d60a266a/cover-image.jpghttps://sciendo.com/journal/JAMRIS140216https://sciendo.com/article/10.14313/jamris/4-2024/31<abstract> <title style='display:none'>Abstract</title> <p>The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN-OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/312024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/29<abstract> <title style='display:none'>Abstract</title> <p>In this article, the control problem for omnidirectional 3-wheel autonomous mobile robots is solved with the use of (i) a nonlinear optimal control method (ii) a flatness-based control approach which is implemented in successive loops. To apply method (i) that is nonlinear optimal control, the dynamic model of the omnidirectional 3-wheel autonomous mobile robots undergoes approximate linearization at each sampling instant with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrix. The linearization point is defined by the present value of the system’s state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the non-linear optimal control method are proven through Lyapunov analysis. To implement control method (ii), that is flatness-based control in successive loops, the state-space model of the omnidirectional 3-wheel autonomous mobile robot is separated into chained subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the preceding (i-th) subsystem become virtual control inputs for the subsequent (i+1-th) subsystem. In turn, exogenous control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The proposed method achieves trajectory tracking and autonomous navigation for the omnidirectional 3-wheel autonomous mobile robots without the need of diffeomorphisms and complicated state-space model transformations.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/292024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/27<abstract> <title style='display:none'>Abstract</title> <p>Metaheuristics, such as evolutionary algorithms (EAs), have been proven to be (also theoretically, see, for example, the works of Michael Vose [<xref ref-type="bibr" rid="j_jamris-4-2024-27_ref_001">1</xref>]) universal optimization methods. Previous works (Zbigniew Skolicki and Kenneth De Jong [<xref ref-type="bibr" rid="j_jamris-4-2024-27_ref_002">2</xref>]) investigated impact of migration intervals on island models of EAs in their works. Here we explore different migration intervals and amounts of migrating individuals, complementing Skolicki and DeJong’s research. In our experiments, we use different ways of selecting migrants and pave the way for further research, e.g., involving different topologies and neighborhoods. We present the idea of the algorithm, show experimental results.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/272024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/33<abstract> <title style='display:none'>Abstract</title> <p>This study proposes an integrated framework for efficient traffic object detection and classification by leveraging advanced deep-learning techniques. The framework begins with the input of video surveillance, followed by an image-acquisition process to extract the relevant frames. Subsequently, a Faster R-CNN (ResNet-152) architecture was employed for precise object detection within the extracted frames. The detected objects are then classified using deep reinforcement learning, specifically trained to identify distinct traffic entities, such as buses, cars, trams, trolleybuses, and vans. The UA-DETRAC dataset served as the primary data source for training and evaluation, ensuring the model’s adaptability to real-world traffic scenarios. Finally, the performance of the framework was assessed using key metrics, including precision, recall, and F1 score, providing insights into its effectiveness in accurately detecting and classifying traffic objects. This integrated approach offers a promising solution to enhance traffic surveillance systems and facilitate improved traffic management and safety measures in urban environments.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/332024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/28<abstract> <title style='display:none'>Abstract</title> <p>The mutual inductance model enables testing of the behavior of power transformers under different operation conditions, especially under the environmental influences on the transformer’s core. This paper presents the results of an investigation of the stress dependence of the magnetic relative permeability of a power transformer. It was observed that under tensile mechanical stresses up to 98 MPa applied to the core, the transformer input current amplitude increases by almost 67%, whereas the transformer’s reactive power increases by 53%. In industrial systems, such changes can potentially lead to unwanted power system shutdowns due to overloading. This effect should be considered during the development of critical power systems.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/282024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/32<abstract> <title style='display:none'>Abstract</title> <p>Pressurized water reactors (PWRs) are the most common and widely used type of reactor, and ensuring the stability of the reactor is of utmost importance. The challenges lie in effectively managing power fluctuations and sudden changes in reactivity that could result in unsafe situations. Reactor power fluctuations can cause changes in behavior. At the same time, the transfer of heat from the fuel to the coolant and reactivity changes resulting from differences in fuel and coolant temperatures can also make the system unpredictable. The primary goal of a power controller used in a nuclear reactor is to sustain the specified power level, which guarantees the security of the power plant. To address these challenges, this paper presents a dynamic model of a PWR and applies several control techniques to the system for power level control. Specifically, a traditional PID controller, a neural network controller, a fuzzy self-tuned PID controller, and a neurofuzzy self-tuned PID controller were individually designed and evaluated to enhance the performance of the reactor power control system under constant and variable reference power. In addition, the robustness of each controller was assessed against time delays and external disturbances. The system was tested with various initial power values to evaluate its performance under different conditions. The results demonstrate that the neuro-fuzzy self-tuned PID controller has the best performance, as well as the fastest response time compared to the other controllers.</p> <p>Furthermore, the intelligent controllers were found to exhibit good robustness against time delays and external disturbances. The system’s stability was not significantly affected by changes in the initial power value, although it had a minor effect on the transient response. Overall, the findings of this study can inform the design and optimization of control systems for PWRs, with the ultimate goal of improving their safety, reliability, and performance.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/322024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/4-2024/30<abstract> <title style='display:none'>Abstract</title> <p>This study focused on the development of an Autonomous Underwater Vehicle (AUV) across four key domains: Mechanical Design, Software, Electronics, and Security. The Mechanical Design phase involved utilizing computer-aided drawing programs to create the AUV model. Important design considerations encompass manufacturability, cost, power, weight, and durability. Comparisons with nominal values of existing market products validated the precision of the produced designs. This study placed particular emphasis on the optimization of model weight, with a focus on ensuring the AUV’s efficiency, lightness, and exceptional maneuverability within underwater environments. The Software stage entailed the development of AUV software to enable sensitive and effective vehicle operations. Efficient functioning, devoid of errors or complications, was imperative to ensure optimal autonomous driving and operational capabilities. Image processing algorithms were incorporated into the software to maintain high accuracy and provide dimensional and geometric information from targeted areas. Furthermore, the software phase involved the development of an image processing algorithm based on color analysis, further augmenting accuracy. The selection of electronic components for the AUV was also a vital consideration, alongside ensuring safety measures at every stage of the UAV’s development.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/4-2024/302024-12-10T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/22<abstract> <title style='display:none'>Abstract</title> <p>Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&amp;O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&amp;O algorithm has some limitations, such as oscillations and steadystate error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m² and 2000w/m²) and see the system response. The performance of the enhanced P&amp;O algorithm has been evaluated using MATLAB simulations.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/222024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/18<abstract> <title style='display:none'>Abstract</title> <p>As the computational and communicational capabilities of edge and IoT devices grow, so do the opportunities for novel machine learning (ML) solutions. This leads to an increase in popularity of Federated Learning (FL), especially in cross-device settings. However, while there is a multitude of ongoing research works analyzing various aspects of the FL process, most of them do not focus on issues concerning operationalization and monitoring. For instance, there is a noticeable lack of research in the topic of effective problem diagnosis in FL systems. This work begins with a case study, in which we have intended to compare the performance of four selected approaches to the topology of FL systems. For this purpose, we have constructed and executed simulations of their training process in a controlled environment. We have analyzed the obtained results and encountered concerning periodic drops in the accuracy for some of the scenarios. We have performed a successful reexamination of the experiments, which led us to diagnose the problem as caused by exploding gradients. In view of those findings, we have formulated a potential new method for the continuous monitoring of the FL training process. The method would hinge on regular local computation of a handpicked metric: the gradient scale coefficient (GSC). We then extend our prior research to include a preliminary analysis of the effectiveness of GSC and average gradients per layer as potentially suitable for FL diagnostics metrics. In order to perform a more thorough examination of their usefulness in different FL scenarios, we simulate the occurrence of the exploding gradient problem, vanishing gradient problem and stable gradient serving as a baseline. We then evaluate the resulting visualizations based on their clarity and computational requirements. We introduce a gradient monitoring suite for the FL training process based on our results.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/182024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/21<abstract> <title style='display:none'>Abstract</title> <p>This article presents a comparison of a classical approach to identification of unstable object and an approach based on artificial neural networks. Model verification is carried out based on the Quanser Qube-Servo object with the use of myRIO real-time controller as the target. It is shown that model identification using neural networks gives a more accurate representation of the object. In addition, the hardware-in-the-loop (HIL) technique is discussed and used, for implementation of the control algorithm.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/212024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/17<abstract> <title style='display:none'>Abstract</title> <p>Federated learning (FL) involves joint model training by various devices while preserving the privacy of their data. However, it presents a challenge of dealing with heterogeneous data located on participating devices. This issue can further be complicated by the appearance of malicious clients, aiming to sabotage the training process by poisoning local data. In this context, a problem of differentiating between poisoned and non-identically-independently-distributed (non-IID) data appears. To address it, a technique utilizing data-free synthetic data generation is proposed, using a reverse concept of adversarial attack. Adversarial inputs allow for improving the training process by measuring clients’ coherence and favoring trustworthy participants. Experimental results, obtained from the image classification tasks for MNIST, EMNIST, and CIFAR-10 datasets are reported and analyzed.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/172024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/26<abstract> <title style='display:none'>Abstract</title> <p>IEEE 802.16 can be seen as a compelling replacement for conventional broadband technologies because its primary goal is to provide Broadband Wireless Access (BWA). The variable and uncertain nature of wireless networks makes it much more challenging to ensure QoS in this network. WiMAX Technology is used to support various quality of services which includes UGS, rtps, nrtps, ertps, and Best Effort. This study employs an IEEE 802.16 network simulator, which offers adaptable and reliable features for assessing a particular QoS parameters for rtps. Achieving better internet performance in real time services is currently a challenge, and it is in need of a present scenario. This work emphasized better internet service, with good quality of service using rtps with Relay Station and Without Relay Station. In this work the CBR packet size, CBR data rate, and data rate with rtps service are fine-tuned for achieving better performance with good quality of service. When comparing uplink connections in rtps with and without relay station, it is found that the throughput in the uplink is 200% greater when using a relay station. The throughput and goodput are evaluated in uploading and downloading with single and multiple subscriber stations and we observed that the multiple subscriber stations in downloading give better performance, as compared to single subscriber stations. The throughput and goodput in single subscriber stations is better than multiple subscriber stations in uploading. The academic researchers and commercial developers can use this analysis to validate different WiMAX Network implementation mechanisms and parameters.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/262024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/23<abstract> <title style='display:none'>Abstract</title> <p>EEG-based emotion classification is considered to separate and observe the mental state or emotions. Emotion classification using EEG is used for medical, security and other purposes. Several deep learning and machine learning strategies are employed to classify the EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here, the input EEG signals are taken from two benchmark datasets,namely DEAP and K-EmoCon datasets. Then, the input EEG signals are pre-processed,and the features are extracted utilizing ContextNet with Global Principal Component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks method. RSTAGNN weight parameters are optimized under the Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The efficacy of the proposed method using the DEAP dataset attains higher accuracy by 24.05%, 12.64% related to existing systems, like Multi-domain feature fusion for emotion classification (DWT-SVM-EEGEA- DEAP), EEG emotion finding utilizing fusion mode of graph CNN with LSTM (GCNN-LSTM-EEG-EA-DEAP) respectively. The efficiency of the proposed method using the K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like Toward Robust Wearable Emotion Realization along Contrastive Representation Learning (CAT-EEG-EA-K-EmoCon) and Human Emotion Recognition using Physiological Signals (CATEEG- EA-K-EmoCon) respectively.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/232024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/25<abstract> <title style='display:none'>Abstract</title> <p>In this paper Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri search optimization (CSO) algorithm, general practitioner-sick person (PS) optimization algorithm are applied for solving factual power loss reduction problem. Natural actions of Atlantic blue marlin are emulated to design the Atlantic blue marlin (ABM) optimization algorithm and populace in the examination space is capriciously stimulated. Boops optimization (BO) algorithm is designed by imitating the stalking physiognomies of Boops. CSO is based on the drive and search behavior of Chironex fleckeri. A general practitioner will treat a sick person with various procedures which have been imitated to model the Projected PS algorithm. Inoculation, medicine and operation are the procedures considered in the PS algorithm. Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri search optimization (CSO) algorithm, general practitioner – sick person (PS) optimization algorithm validated in IEEE 57, 300 systems and 220 KV network. Factual power loss lessening, power divergence restraining, and power constancy index amplification have been attained.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/252024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/24<abstract> <title style='display:none'>Abstract</title> <p>This study addresses the pressing need for improved stock price prediction models in the financial markets, focusing on the Indonesian stock market. It introduces an innovative approach that utilizes the custom activation function RunReLU within a concave long short-term memory (LSTM) framework. The primary objective is to enhance prediction accuracy, ultimately assisting investors and market participants in making more informed decisions. The research methodology used historical stock price data from ten prominent companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. Evaluation metrics such as RMSE, MAE, MAPE, and R2 were employed to assess model performance. The results consistently favored the RunReLUbased model over the ReLU-based model, showcasing lower RMSE and MAE values, higher R2 values, and notably reduced MAPE values. These findings underscore the practical applicability of custom activation functions for financial time series data, providing valuable tools for enhancing prediction precision in the dynamic landscape of the Indonesian stock market.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/242024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/19<abstract> <title style='display:none'>Abstract</title> <p>The evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as the gold standard for assessing auditory function. This method enables the detection of hearing impairment, which may be further identified as conductive, sensorineural, or mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone audiometry samples that have been labeled by professional audiologists in order to develop an automatic classifier of hearing loss type. The tested models include random forest, support vector machines, logistic regression, stochastic gradient descent, decision trees, convolutional neural network (CNN), feedforward neural network (FNN), recurrent neural network (RNN), gated recurrent unit (GRU) and long short-term memory (LSTM). The presented work also investigates the influence of training dataset augmentation with the use of a conditional generative adversarial network on the performance of machine learning algorithms, and examines the impact of various standardization procedures on the effectiveness of deep learning architectures. Overall, the highest classification performance was achieved by LSTM, with an out-of-training accuracy of 97.56%.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/192024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/3-2024/20<abstract> <title style='display:none'>Abstract</title> <p>In this study, we explore the implications of dataset limitations in semantic knowledge-driven machine translation (MT) for intelligent virtual assistants (IVA). Our approach diverges from traditional single-best translation techniques, utilizing a multi-variant MT method that generates multiple valid translations per input sentence through a constrained beam search. This method extends beyond the typical constraints of specific verb ontologies, embedding within a broader semantic knowledge framework.</p> <p>We evaluate the performance of multi-variant MT models in translating training sets for Natural Language Understanding (NLU) models. These models are applied to semantically diverse datasets, including a detailed evaluation using the standard MultiATIS++ dataset. The results from this evaluation indicate that while multivariant MT method is promising, its impact on improving intent classification (IC) accuracy is limited when applied to conventional datasets such as MultiATIS++. However, our findings underscore that the effectiveness of multivariant translation is closely associated with the diversity and suitability of the datasets utilized.</p> <p>Finally, we provide an in-depth analysis focused on generating variant-aware NLU datasets. This analysis aims to offer guidance on enhancing NLU models through semantically rich and variant-sensitive datasets, maximizing the advantages of multi-variant MT.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/3-2024/202024-09-12T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/2-2022/13<abstract> <title style='display:none'>Abstract</title> <p>The rapid development of robotics involves human-robot interaction (HRI). It is a necessary to assess user satisfaction to develop HRI effectively. Thus, HRI calls for interdisciplinary research, including psychological instruments such as survey questionnaire design. Here, we present a factor analysis of a Polish version of the Godspeed Questionnaire (GSQ) used to measure user satisfaction. The questionnaire was administered to 195 participants. Then, factor analysis of the GSQ was performed. Finally, reliability analysis of the Polish version of the GSQ was done. The adapted version of the survey was characterized by a four-factor structure, i.e., anthropomorphism, perceived intelligence, likeability, and perceived safety, with good psychometric properties.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/2-2022/132023-05-29T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/2-2022/12<abstract> <title style='display:none'>Abstract</title> <p>In alignment with smart city initiatives, Malaysia is shifting its educational landscape to a smart learning environment. The Ministry of Education (MoE) has made History a mandatory subject for passing the Malaysian Certificate of Education to grow awareness and instil patriotism among Malaysian students. However, History has been known as one of the difficult subjects to study for many students. On the other hand, the Malaysian Government Education Blueprint 2013-2025 seeks to “leverage ICT scale up quality learning” across the country. Therefore, this study aims to identify the factors that influence the intention to use Augmented Reality (AR) for mobile learning in learning History subject among secondary school students in Malaysia. Quantitative approach has been chosen as the research method for this study. A direct survey was conducted on 400 secondary school students in one of the smart cities in Malaysia as the target respondents. The collected data are analysed through descriptive statistics and Multiple Linear Regression analysis by using Statistical Package for the Social Sciences. Based on the results, the identified factors that influence the intention to use AR for mobile learning in learning History subject are Gender, Perceived Usefulness, Perceived Enjoyment, and Attitude Towards Use. The identified factors can be a good reference for schools and teachers to strategize their teaching and learning methods in pertaining to History subject among secondary school students in Malaysia. Future studies may include the study of various types of schools in Malaysia and explore more moderating effects of demographic factors.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/2-2022/122023-05-29T00:00:00.000+00:00https://sciendo.com/article/10.14313/jamris/2-2022/10<abstract> <title style='display:none'>Abstract</title> <p>We present the results of an exploratory analysis of human attitudes toward the social robot Vector. The study was conducted on natural language data (2,635 comments) retrieved from Reddit and YouTube. We describe the tag-set used and the (manual) annotation procedure. We present and compare attitude structures mined from Reddit and YouTube data. Two main findings are described and discussed: almost 20% of comments from both Reddit and YouTube consist of various manifestations of attitudes toward Vector (mainly attribution of autonomy and declaration of feelings toward Vector); Reddit and YouTube comments differ when it comes to revealed attitude structure – the data source matters for attitudes studies.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.14313/jamris/2-2022/102023-05-29T00:00:00.000+00:00en-us-1