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This dictionary contains terms from the fields of automatic control, which includes mathematical modelling, simulation of dynamic systems, automation technology with its corresponding elements, and robotics. It also includes signal processing, information technologies and production technologies. The terminological dictionary is primarily aimed at experts and students who deal with control technology and dynamic systems in both technical and non-technical domains. To be able to use the dictionary, at least basic knowledge in this field is required. In the dictionary users will find concise terminological definitions. A given concept may be expressed by different terms; therefore, cross-references are used.

Pharmacovigilance is vital for post-market drug safety monitoring. Traditional trials inadequately capture adverse reactions. Patient-generated opinions offer valuable insights but pose challenges due to limited availability, inefficient annotation scheme, and volume of drug reviews, indicating the need of automation. To address the challenge posed by the limited availability of annotated drug reviews, we introduce a novel mechanism of annotating drug reviews dataset through human intervention, specifically tailored for Aspect Term Extraction (ATE) and Polarity Detection (PD) in three medical conditions: Depression, Arthritis and Birth control, thereby making it publicly available to facilitate future research. We first designed, ATE drug , an automated annotation scheme with minimal human intervention by employing an expert-driven rule-based approach. We further deploy ATE drug for sequence labelling and classification tasks in drug reviews. The automatically labeled data is further used for training and testing of deep learning models: BERT, BioBERT, and ClinicalBERT, for clinical use. We manually evaluate the results through annotator agreement to validate the effectiveness of ATE drug . The labeled dataset is further used to construct transformer-based models. Our proposed model is reliable, safe and trustworthy for healthcare domain, thereby eliminating the compromises with hallucination and data security through generative AI models. All experimental codes and data are available on GitHub ( https://github.com/CMOONCS/ateDrug.git ).

This paper discusses optimal design of the series proportional–integral–derivative–accelerative (PIDA) controller for integral-plus-dead-time (IPDT) plants. The article starts with the design of disturbance reconstruction and compensation based on proportional-derivative-accelerative (PDA) stabilizing controllers. It shows that by introducing positive feedback by a low-pass filter from the (limited) output of the stabilizing PDA controller, one gets disturbance observer (DOB) for the reconstruction and compensation of input disturbances. Thereby, the DOB functionality is based on evaluating steady-state controller output. This DOB interpretation is in full agreement with the results of the analysis of the optimal setting of the stabilizing PDA controller and of its expanded PIDA version with positive feedback from the controller output. By using the multiple real dominant pole (MRDP) method, it confirms that the low-pass filter time constant in positive feedback must be much longer than the dominant time constant of the stabilized loop. This paper also shows that the constrained PIDA controller with the MRDP setting leads to transient responses with input and output overshoots. Experimentally, such a constrained series PIDA controller can be shown as equivalent to a constrained MRDP tuned parallel PIDA controller in anti-windup connection using conditional integration. Next, the article explores the possibility of removing overshoots of the output and input of the process achieved for MRDP tuning by interchanging the parameters of the controller transfer function, which was proven as very effective in the case of the series PID controller. It shows that such a modification of the controller can only be implemented approximately, when the factorization of the controller numerator, which gives complex conjugate zeros, will be replaced by a double real zero. Neglecting the imaginary part and specifying the feedback time constant with a smaller approximative time constant results in the removal of overshoots, but the resulting dynamics will not be faster than for the previously mentioned solutions. A significant improvement in the closed-loop performance can finally be achieved by the optimal setting of the constrained series PIDA controller calculated using the performance portrait method. This article also points out the terminologically incorrect designation of the proposed structure as series PIDA controller, because it does not contain any explicit integral action. Instead, it proposes a more thorough revision of the interpretation of controllers based on automatic reset from the controller output, which do not contain any integrator, but at the same time represent the core of the most used industrial automation. In the end, constrained structures using automatic reset of the stabilizing controller output can ensure a higher performance of transient responses than the usually preferred solutions based on parallel controllers with integral action that, in order to respect the control signal limitation, must be supplemented with anti-windup circuitry. The excellent properties of the constrained series PIDA controller are demonstrated by an example of controlling a thermal process and proven by the circle criterion of absolute stability.

In this paper, we present an unsupervised dependency-based approach to extract semantic relations to be applied in the context of automatic generation of multiple choice questions (MCQs). MCQs also known as multiple choice tests provide a popular solution for large-scale assessments as they make it much easier for test-takers to take tests and for examiners to interpret their results. Manual generation of MCQs is a very expensive and time-consuming task and yet they often need to be produced on a large scale and within short iterative cycles. We approach the problem of automated MCQ generation with the help of unsupervised relation extraction, a technique used in a number of related natural language processing problems. The goal of Unsupervised relation extraction is to identify the most important named entities and terminology in a document and then recognise semantic relations between them, without any prior knowledge as to the semantic types of the relations or their specific linguistic realisation. We use these techniques to process instructional texts and identify those facts (terminology, entities, and semantic relations between them) that are likely to be important for assessing test-takers’ familiarity with the instructional material. We investigate an approach to learn semantic relations between named entities by employing a dependency tree model. Our findings show that an optimised configuration of our MCQ generation system is capable of attaining high precision rates, which are much more important than recall in the automatic generation of MCQs. We also carried out a user-centric evaluation of the system, where subject domain experts evaluated automatically generated MCQ items in terms of readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The results of this evaluation make it possible for us to draw conclusions about the utility of the approach in practical e-Learning applications.

Control systems guarantee safe train operations by (1) monitoring and supervising train speed, (2) defining the next allowable target position, and (3) activating automatic braking systems. Two different train control systems, (1) Communications-Based Train Control (CBTC) and (2) European Train Control Systems (ETCS), are usually implemented for railway operations. Among those systems, the CBTC is applied for light railway and urban mass-transit systems, whereas ETCS is used for heavy railway systems. These two train control systems have been used worldwide for more than 20 years. Although both systems share a common field of application, their scope, functionalities, and automation aims differ. This paper reviews the CBTC and ETCS systems, and their underlying moving principles. The applicability, terminology, differences, and challenges with regard to levels of implementation, radio communication systems, train integrity, and interoperability are analysed in detail. Based on this analysis, potential characteristics are identified that could be adopted from these systems to improve railway operation.

In the present paper, we study the infinitesimal symmetries of the model of two Riemannian manifolds ( M ,  g ) and ( M ^ , g ^ ) rolling without twisting or slipping. We show that, under certain genericity hypotheses, the natural bundle projection from the state space Q of the rolling model onto M is a principal bundle if and only if M ^ has constant sectional curvature. Additionally, we prove that when M and M ^ have different constant sectional curvatures and dimension n ≥ 3 , the rolling distribution is never flat, contrary to the two dimensional situation of rolling two spheres of radii in the proportion 1 : 3 , which is a well-known system satisfying É. Cartan’s flatness condition.

ICT has the capability to improve the quality of life experienced by our citizens through education, training, new services, innovation and automated control systems. ICT especially the emerging technologies investigated here are of immense importance to supporting education, health, innovation and infrastructure. AI and Big Data systems show great potential for achieving the United Nations Sustainable Development Goals which currently requires immediate global action in areas like climate change, poverty and pollution to ensure that additional damage to societies and the planet is prevented.Standardization is key to sustainability in the fourth industrial revolution. As standards are living documents, they evolve with the technologies and provide an additional pathway to technological innovation. Application solutions employing AI/Big Data technologies or utilising platforms based on these technologies provide interoperability and quality assurance through standardization. These digitally disruptive technologies are constantly renewing the way individual businesses or sometimes entire industries work by tearing down established products/processes/services and creating new ones.In the next few years, the emerging technologies driving digital disruption including artificial intelligence, cloud computing, the Internet of Things, Big Data Analytics will create new synergies and collaborations that will cause further acceleration of uptake. This new technological advancement is expected to create further optimizations and efficiencies in nearly every industry sector. How can we prepare for this digital disruption caused by AI and Big Data Analytics? The best response is to transform your business (products/processes/services) into a reactive, responsive, dynamic living enterprise that adapts to new threats and opportunities. Building a standardization ethos into our enterprises introduces innovation, structure, quality, scalability and interoperability at the heart of the business. Standards are what we put into our products, processes and services, while confidence is what our customers get out.The articles presented in this special issue look at a diverse set of Artificial Intelligence and Big Data challenges. In Paper 1, the impact of Emerging Technologies is investigated through the prism of specific use cases where emerging technologies are employed. This impact is discussed in relation to progress towards achievement of each of the United Nations Sustainable Development Goals. Emerging technologies like AI, Big Data, IoT and 5G can have an impact on large scale challenges and opportunities economically, socially and environmentally. In order for these technologies to mature and scale, there is a clear and present need to address sustainability, fairness, safety, security, interoperability, inclusiveness, transparency, efficiency, and usability. Emerging technology standardization and the work of the international SDOs and SSOs is crucial to provide the sustainable society that our citizens deserve. Standards are living documents and evolve as the technologies evolve, which allows stakeholders to strengthen the synergetic relationship between technological innovation and standardization.Paper 2 Describes key features of ICT for Learning, Education and Training and presents questions that need to be addressed in parallel with standardization where AI is part of the solution. Scope statements require precision and clarity to be robust. In the broader standardization ecosystem will the terminology and associated definitions stand the test of time? In this new era of emerging technologies like AI, new and multiple frontiers are being created that introduces new complexity even into core terminology. AI can deliver a net benefit where standardization of AI in ICT for Learning, Education and Training is employed.The challenges examined in Paper 3 are around developing international standards for Trustworthy AI to be global applicable and to address the ethical questions key to building trust. Firstly the authors examine the validity of grounding standards that aim for international reach on human right agreements, and secondly they discuss the recent proposals from the OECD, the EU and the IEEE on ethical governance of Trustworthy AI systems in terms of their scope and use of normative language. A preliminary minimal model for the functional roles relevant to Trustworthy as a framing for further standards development in this area is proposed.To complete the AI Standardization suite of four, the final paper presents a comprehensive Landscape assessment of AI Standardization. Results of the analysis of 5 international and European Standards Development Organisations (SDOs) IEEE, ISO/IEC, ITU-T, ETSI and CEN-CENELEC developing standards in the field of AI are presented. The same analysis is carried out and results presented for two Standards Settings Organisations (SSOs): W3C and IRTF and their active groups. The main outcomes that the number of working groups chaired by Europeans is significant and that there is room for European experts contributing to ongoing and future standardization work.

Quality attribute requirements are important both for customer and end-user satisfaction and for driving software system design. Yet asserting their importance raises many other questions. In particular, using quality attribute information in practice isn't obvious. Here, we consider two aspects of using such information: communicating with stakeholders about quality attributes and incorporating quality attribute requirements into existing analysis and design methods.

As more scientific work is published, it is important to improve access to the biomedical literature. Since 2000, when Medical Subject Headings (MeSH) Concepts were introduced, the MeSH Thesaurus has been concept based. Nevertheless, information retrieval is still performed at the MeSH Descriptor or Supplementary Concept level. The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. Three sets of queries were built for thirty-two rare diseases and twenty-two chronic diseases: (1) using PubMed Automatic Term Mapping (ATM), (2) using Catalog and Index of French-language Health Internet (CISMeF) ATM, and (3) extrapolating the MEDLINE citations that should be indexed with a MeSH Concept. Type 3 queries retrieve significantly fewer results than type 1 or type 2 queries (about 18,000 citations versus 200,000 for rare diseases; about 300,000 citations versus 2,000,000 for chronic diseases). CISMeF ATM also provides better precision than PubMed ATM for both disease categories. Using MeSH Concept indexing instead of ATM is theoretically possible to improve retrieval performance with the current indexing policy. However, using MeSH Concept information retrieval and indexing rules would be a fundamentally better approach. These modifications have already been implemented in the CISMeF search engine.

Bertrand Meyer recently proposed seven principles of software testing. Other sets of principles embraced by testing professionals worldwide suggest that Meyer's list can be improved. One such set is the International Software Testing Qualifications Board Certified Tester Foundation Level Syllabus.