Autonomic System Research Articles

Reduction-Induced Self-Propelled Oscillatory Motion of Perylenediimides on Water.

The emergence of macroscopic self-propelled oscillatory motion based on molecular design has attracted continual attention in relation to autonomous systems in living organisms. Herein, a series of perylenediimides (PDIs) with various imide side chains was prepared to explore the impact of molecular design and alignment on the self-propelled motion at the air-water interface. When placed on an aqueous solution containing a reductant, a solid disk of neutral PDI was reduced to form the water-soluble, surface-active PDI dianion species, which induces a surface tension gradient in the vicinity of the disk for self-propelled motion. We found that centimeter-scale oscillatory motion could be elicited by controlling the supply rate of PDI dianion species through the reductant concentration and the structure of the imide side chains. Furthermore, we found that the onset and speed of the self-propelled motion could be changed by the crystallinity of PDI at the water surface. This design principle using π-conjugated molecules and their self-assemblies could advance self-propelled, non-equilibrium systems powered by chemical energy.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Reduction‐Induced Self‐Propelled Oscillatory Motion of Perylenediimides on Water

AbstractThe emergence of macroscopic self‐propelled oscillatory motion based on molecular design has attracted continual attention in relation to autonomous systems in living organisms. Herein, a series of perylenediimides (PDIs) with various imide side chains was prepared to explore the impact of molecular design and alignment on the self‐propelled motion at the air‐water interface. When placed on an aqueous solution containing a reductant, a solid disk of neutral PDI was reduced to form the water‐soluble, surface‐active PDI dianion species, which induces a surface tension gradient in the vicinity of the disk for self‐propelled motion. We found that centimeter‐scale oscillatory motion could be elicited by controlling the supply rate of PDI dianion species through the reductant concentration and the structure of the imide side chains. Furthermore, we found that the onset and speed of the self‐propelled motion could be changed by the crystallinity of PDI at the water surface. This design principle using π‐conjugated molecules and their self‐assemblies could advance self‐propelled, non‐equilibrium systems powered by chemical energy.

When Own Interest Stands Against the “Greater Good” – Decision Randomization in Ethical Dilemmas of Autonomous Systems That Involve Their User’s Self-Interest

Autonomous systems (ASs) decide upon ethical dilemmas and their artificial intelligence as well as situational settings become more and more complex. However, to study common-sense morality concerning ASs abstracted dilemmas on autonomous vehicle (AV) accidents are a common tool. A special case of ethical dilemmas is when the AS’s users are affected. Many people want AVs to adhere to utilitarian programming (e.g., to save the larger group), or egalitarian programming (i.e., to treat every person equally). However, they want their own AV to protect them instead of the “greater good”. That people reject utilitarian programming as an AS’s user while supporting the idea from an impartial perspective has been termed the “social dilemma of AVs”. Meanwhile, preferences for another technical capability, which would implement egalitarian programming, have not been elicited for dilemmas involving self-interest: decision randomization. This paper investigates normative and descriptive preferences for a self-protective, self-sacrificial, or randomized choice by an AS in a dilemma where people are the sole passenger of an AV, and their survival stands against the survival of several others. Results suggest that randomization may mitigate the “social dilemma of AVs” by bridging between a societally accepted programming and the urge of ASs’ users for self-protection.

Development of a Semi-Autonomous Pulse and Receive Concrete Inspection System

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

Towards Integrated Safety Assurance Methodology for Autonomous Vessel Navigation Systems

Abstract Safety assurance remains paramount for the realization of autonomous vessels. A robust assurance methodology that can provide traceability throughout the design and verification process is necessary to demonstrate safety equivalence to that of conventional ships. However, there are few references that propose a holistic safety assurance scheme for autonomous ships, using actual engineering processes of ships as examples. This study proposes a design and development method for an autonomous navigation system that integrates Model-Based Systems Engineering (MBSE), System Theoretic Process Analysis (STPA), Failure Modes and Effects Analysis (FMEA), and several verification methods including simulation-based tests to develop a comprehensive safety assurance scheme. This safety assurance scheme is being conducted as a case study for a newly constructed autonomous vessel. First, STPA can be performed on the conceptual design established by MBSE to extract additional safety requirements from the identified loss scenarios. Focusing on the process model in the loss scenario description leads to a deeper understanding of the system behavior. FMEA in addition identify potential component failure modes and their impact on system safety. The multi-level requirements that emerge from these activities are validated in test scenarios. Simulators are used to evaluate whether the autonomous navigation system’s safety can meet predefined pass criteria in some of the scenarios. These activities ensure traceability from safety goals to design decisions, allowing design changes and their impact on safety to be evaluated throughout the development lifecycle and allowing for more systematic ongoing monitoring during operations.

The development and status of self-evaluation as a component of school inspection and quality assurance: A comparison across four countries

Objective and rationaleThe increasing prominence of school self-evaluation (SSE) as a pivotal element in global quality assurance practices is attributed to its cost-effectiveness, relevance to school contexts, empowerment of schools in managing and leading the school self-evaluation processes and holding them accountable for achievement standards. This paper aims to explore various approaches and SSE practices through the evolution and current status of school self-evaluation within the realms of school inspection and quality assurance in Dubai, Ireland, New Zealand and Pakistan. The paper emphasises a comparative perspective to showcase SSE models from these countries as pioneering initiatives for quality assurance in educational improvement, especially for nations where SSE practices are still evolving. MethodsThis study employs literature review, document analysis, and semi-structured interviews with school leaders (n = 32) to offer insights into current school self-evaluation practices in these four countries through a comparative analysis. These methods were chosen in line with the specific aims and research questions of the paper which are to provide an overview from the research literature of the development and current status of SSE as an element of school evaluation in each country studied. To analyse, compare and contrast policy in the area in each case and to obtain, through interviews, rich deep data and findings on the attitude and opinions of key personnel in each country on SSE as a policy and practice. ResultsSchool self-evaluation (of varying types) is being built into each school evaluation system and becoming an integral part of inspection and quality assurance, and it is argued here, broadly moving along a similar spectrum in each case. However, for now, SSE practices are different in all four countries, usually due to the stage of development of the school inspection system and, above all, the overall structure of their respective educational landscapes— that is to say, whether it is a centralised, controlled and hierarchical education system or a decentralised and autonomous system. ConclusionThe study suggests that it is possible to integrate school self-evaluation into a wide range of educational systems and philosophies, eventually leading to educational improvement in any country following the case study countries discussed as models.

Rigid Formation Control on a Sphere: A Heterogeneous System Approach.

We consider a heterogeneous multiagent system for tracking multiple targets with a rigid formation on a unit sphere, where the targets and chasing agents are governed by single- and double-integrator models, respectively. To make asymptotic rendezvous between agents and their corresponding targets, we use an autonomous system consisting of attraction forces and velocity alignments. If the target's position, velocity, and acceleration information are available, we derive a multiagent system for complete rendezvous and obtain its exponential convergence result. If we have only the location and velocity information of the targets, we provide an autonomous system for practical rendezvous and the corresponding mathematical analysis. To prove the main results, we employ frame-rotation-structure decomposition for the double-integrator model and the geometric properties of a rigid formation on a sphere. We also provide numerical simulations to confirm our mathematical results and apply them to multiagent dynamics with a rigid formation that patrols the boundary line for a certain area on the sphere.

Safety Assurance of Maritime Autonomous Surface Ships

Abstract Safety of Maritime Autonomous Surface Ships (MASS) is inherently complex owing to the dynamic and unpredictable nature of the maritime environment. This is exacerbated by the pace of change in enabling technologies and their disruptive impact. Integral to the safety assurance of MASS is assessment and mitigation of risk. The International Maritime Organisation (IMO) defines Risk as “the combination of frequency and severity of the consequence”. The advent of software-controlled systems has introduced difficulties in quantifying frequencies; an issue which is more prevalent in the context of autonomy due to the complex interaction between control algorithms and the variabilities of the environment in which they are deployed. Within complex autonomous systems, therefore it is challenging to perform quantitative risk assessments using failure rates, which gives rise to a need for different techniques to assess risk. Novel risk assessment methodologies such as those presented in the European Maritime Safety Agency (EMSA) Risk Based Assessment Tool (RBAT) combined with conventional risk assessment techniques can offer a solution to this challenge. Compliance with legislation and standards is another cornerstone of safety assurance, which is also inherently difficult for MASS vessels owing to their having been developed for conventional vessels. A goal-based approach to legislation compliance could present an interim solution until the IMO goal-based Maritime Autonomous Systems (MAS) provides harmonisation across the industry. Whilst MASS Safety assurance is non-trivial, there are solutions to many of the challenges faced; this paper examines the predominant challenges and presents potential solutions.

Automation in the Maritime Transport System – A Framework for Planning Resilient Operations

Abstract Maritime transport systems (MTS) are becoming increasingly automated, integrated and complex. New technology, automation and autonomous systems will contribute to safer, greener, and more efficient transport systems. However, the advancements pose new challenges, and it is a need to understand and manage the potential brittleness and risks. Uncertainties and the unknown should be prepared for when planning the implementation of new technology. Hence, this paper presents a framework to support the planning for resilient operations when introducing autonomy in a MTS. The framework is based on a resilient approach and is the first draft of a framework being developed within the research project Maritime Resilience Management of an Integrated Transport system (MARMAN). The approach is intended to facilitate a process where MTS actors work systematically to identify challenges, clarify potential future measures, and prepares for integrating automation and autonomy in a system. The method emphasizes the needs for cooperation, coordination and integrated planning across different transport modalities, organizational boundaries and stakeholders. But also between humans and technology, focusing on the different levels of planning (i.e. strategic, tactical and operational), where the main objectives will be to increase resilience by coping with potential critical events. In addition, continuous learning and commitment between actors and how to use the new technology are crucial for enhancing resilience.

Implementation of Respect for Autonomy in Hospital Services Within the Indonesia National Health Insurance System

The principle of patient autonomy forms the foundation of medical ethics. However, its exploration within the context of national health insurance systems in developing countries remains under-researched. This study aimed to evaluate respect for patient autonomy within Indonesia's National Health Insurance (NHI/JKN) system. The study using a qualitative research interview analyzed thematically. Conducted in Depok, West Java, the study involved in-depth semi-structured interviews with 18 participants, encompassing patients from first-level health facilities (FLHF), general practitioners at FLHF, specialist doctors, and management of referral hospital (RH) officers, and staff members of the JKN. The data were transcribed and analyzed using a thematic approach. The study revealed substantial underutilization of respecting patient autonomy within medical contexts under the JKN. Five themes emerged: challenges in the referral system, knowledge and information dissemination, decision-making and autonomy, quality of healthcare services, and systemic constraints and impact. These themes highlight the lack of patient awareness, restricted medical choices, the dominant role of paternalism (a system in which the government or a person in a position of authority makes decisions for other people) in healthcare decisions, and improper informed consent process. The findings suggest that the principles of beneficence and paternalism often overshadow respect for patient autonomy in the JKN system. This raises concerns about the ethical aspect of patient treatment, particularly the need for greater focus on patient autonomy and shared decision-making to align more closely with global medical ethics practices. This study contributes to understanding autonomy in national health insurance systems in developing countries. It highlights the need for systemic reforms to enhance healthcare efficiency and effectiveness while respecting patient autonomy.

Counterfactual-based two-stage trajectory prediction framework for autonomous vehicle navigation systems

Abstract Accurate prediction of a vehicle’s trajectory is essential for autonomous driving and robotic operations in a complex, dynamic environment. However, current methods that rely on historical and environmental cues to predict future trajectories make the inner distance between testing and training environments often overlooked. To address this issue, we propose a counterfactual analysis method specifically designed for vehicle trajectory prediction. We begin by constructing a causal graph that includes historical trajectories, future trajectories, and environmental interactions. This graph captures the causal relationships between these elements. Our approach directly intervenes on the trajectory itself, effectively cutting off the inference from the environment to the trajectory. By doing so, we mitigate the negative impact of environmental bias. Furthermore, by comparing observed and intervened trajectory clues, we highlight the influence of environmental distance. Our goal is to improve the accuracy of trajectory prediction. Our counterfactual analysis module integrates seamlessly into various baseline prediction models. Experimental data demonstrates that our method outperforms others on publicly available Argoverse2 motion forecasting benchmarks.

Adaptive Autonomic and Neuroplastic Control in Diabetic Neuropathy: A Narrative Review.

Type 2 diabetes mellitus (T2DM) is a worldwide socioeconomic burden, and is accompanied by a variety of metabolic disorders, as well as nerve dysfunction referred to as diabetic neuropathy (DN). Despite a tremendous body of research, the pathogenesis of DN remains largely elusive. Currently, two schools of thought exist regarding the pathogenesis of diabetic neuropathy: a) mitochondrial-induced toxicity, and b) microvascular damage. Both mechanisms signify DN as an intractable disease and, as a consequence, therapeutic approaches treat symptoms with limited efficacy and risk of side effects. Here, we propose that the human body exclusively employs mechanisms of adaptation to protect itself during an adverse event. For this purpose, two control systems are defined, namely the autonomic and the neural control systems. The autonomic control system responds via inflammatory and immune responses, while the neural control system regulates neural signaling, via plastic adaptation. Both systems are proposed to regulate a network of temporal and causative connections which unravel the complex nature of diabetic complications. A significant result of this approach infers that both systems make DN reversible, thus opening the door to novel therapeutic applications.

Convolutional-Neural-Network-Based Autonomous Navigation of Hera Mission Around Didymos

The European Space Agency (ESA)’s Hera mission requires autonomous visual-based navigation in order to safely orbit around the target binary asteroid system Didymos and its moon Dimorphos in 2027. Nevertheless, the performance of optical-based navigation systems depends on the intrinsic properties of the image, such as high Sun phase angles, the presence of other bodies, and, especially, the irregular shape of the target. Therefore, to improve the navigation performance, thermal and/or range measurements from additional onboard instruments are usually needed to complement optical measurements. However, this work addresses these challenges by developing a fully visual-based autonomous navigation system using a convolutional-neural-network (CNN)-based image processing (IP) algorithm and applying it to the detailed characterization phase of the proximity operation of the mission. The images taken by the onboard camera are processed by the CNN-based IP algorithm that estimates the position of the geometrical centers of Didymos and Dimorphos, the range from Didymos, and the associated covariances. The results show that the developed algorithm can be used for a fully visual-based navigation for the position of the Hera spacecraft around the target with good robustness and accuracy.

ENHANCING ADAPTIVE LEARNING AND DECISION-MAKING SYSTEMS USING SWARM INTELLIGENCE AND DEEP LEARNING FOR ADVANCED AI APPLICATIONS

The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.

Latvian Sports Regulation in Transition: Navigating Historical Evolution and Contemporary Development Trends

This comprehensive analysis delves into the intricate realm of sports law and policies in Latvia, exploring the historical evolution, administrative framework, and policy landscapes that shape the country's sports sector. The text highlights Latvia's transition from the Centralized Soviet Model to a more autonomous sports system post-independence, reflecting the nation's historical context and pursuit of sporting excellence. It examines the interaction between EU regulations and internal sports governance, emphasizing the balance between market-regulating rules and legitimate sports governance. The study also discusses recent legislative amendments in response to geopolitical events that has shaken the Eastern Europe, such as the Russian invasion of Ukraine, showcasing Latvia's proactive approach to safeguarding national interests and ethical standards in sports. Furthermore, insights into the implementation of National Anti-Doping Regulations underscore Latvia's commitment to maintaining integrity in sports. The administrative framework involving various stakeholders, including governmental bodies and non-governmental organizations, is analysed to provide a holistic view of sports policy planning and stakeholder engagement in Latvia. Overall, this research sheds light on the evolving landscape of sports law in Latvia and the country's efforts to promote athletic development, fair play, and good governance in sports. The findings will contribute to a deeper understanding of the factors and forces shaping the sports sector in the country. Keywords: sports law and geopolitics, sports governance, sports policies, EU sports law, Latvia

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Energy Harvesting Deadline Monotonic Approach for Real-time Energy Autonomous Systems

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems.

Comparison of methods for using risk information in path planning for autonomous surface vessels

Abstract Path planning is an essential part of autonomous surface vessel (ASV) operations. Risk models can be used to give estimates of risk related to the operation of technical systems, such as ASVs. Combining risk models directly with control systems can be a way to make autonomous systems more capable of assessing and evaluating risk, and in this way allow them to make better decisions. Focusing on path planning, this study aims at investigating how an online risk model used to inform an ASV of risk compares to a static safety domain. The results show that the paths selected when using the risk model can consider different environmental conditions, and the choice of path can be adapted based on how conservative the ASV should be with respect to risk. In this way the risk model provides a more detailed basis for making decisions than the static safety domain.

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.