Advancing autonomous swarm behavior in a simulated anti-access area denial environment

Army senior military leaders are invested in acquiring modernized aerial platforms and equipment to augment the US Army’s ability to overcome Anti-Access Area Denial (A2AD) threats imposed by modern Integrated Air Defense Systems (IADS). A prominent element of this modernization effort is the employment of autonomous drones to defeat IADS threats while minimizing risk to Army Soldiers. This research utilizes a framework for classifying the levels of autonomous capability along three dimensions: the ability to act alone, the ability to cooperate, and the ability to adapt. A virtual combat model, created using the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), simulates the engagement between an enemy IADS and a friendly formation comprised of autonomous drones, attack helicopters, and a Long Range Precision Fires (LRPF) capability. A designed experiment evaluates drone performance with varying levels of autonomy. The experimental results reveal that low levels of autonomy yield a 20.74% increase in survivability and a 5.52% increase in lethality.

The legal debate surrounding the development and deployment of autonomous weapons systems (aws) has stagnated in recent years, having arguably hit the hard limits of legal doctrine. At the heart of this impasse lies the focus upon autonomy as both the innovative and defining feature of aws. Thus, the autonomy of the weapons system places it in a legally liminal zone between agent and object, revealing a set of legal problems that revolve around issues of control, influence, responsibility and liability, and questions of legal compliance that follow from the prospect of autonomous lethal decision-making. This paper seeks to explore alternative framings to the same underlying technology as a means of escaping the limits imposed by the autonomy framework that has dominated the debate to date, and to examine the consequences that flow from pursuing these approaches from legal and regulatory perspectives. In particular, emphasis is placed upon the networks approach, and the systems approach, which this paper sets out and differentiates from the orthodox emphasis upon autonomy. These alternative approaches suggest that the legal problems arising from the autonomy framing are the easiest set of issues to address, insofar as these frame legal problems, while the networks and systems approaches seem to touch upon legal mysteries to which no ready legal or regulatory responses can be made. Rather than dismiss the network and systems approaches, however, this paper suggests that appropriate, adequate and robust legal and regulatory responses must consider the insights and challenges that these approaches pose, and that pursuing these approaches will lead to powerful converging arguments supporting a moratorium on the deployment of aws.

This study is aimed assessing the English pre-service teachers’ communication ability at teacher training and education faculty. It was done at Universitas Kristen Indonesia within 3 months. This study is a quantitative study and the results of the study are reported descriptively to describe the findings obtained. The study involved respondents of 116 students who were taken using the purposive cluster sampling technique. The instrument of the study is a set of questionnaire which was divided in to two parts (demographics and generic skills data). The data was analyzed by inferential statistical analysis. The finding of this study is that there is no significant difference between the level of Communication ability of male and female students, and there is a significant relationship between the level of communication ability and student achievement. Then, it is concluded that the level of confidence in communication ability of education students is at a high level. However, the ability to communicate in English is still at a moderate level that is less satisfactory.Keywords: Pre-service teachers; communication; ability; gender.

Embry-Riddle Aeronautical University is working on an ongoing project, Resilient Autonomous Systems (RAS), supported by the Air Force Research Lab (AFRL). The objective of this project is to develop autonomous vehicle command and control (C2) technologies that demonstrate increased resilience in Anti-Access Area Denial (A2/AD) environments. Current automated solutions that offer little autonomous re-planning capability can be inflexible in handling dynamic scenarios in these environments. In this case, increased resiliency is defined as the ability of the system to better operate at or above an acceptable level of performance even in unfavorable environments, such as when encountering intelligent adversaries using Electronic Warfare (EW) and Integrated Air Defenses (IAD). The ERAU team has outlined a number of scenarios that set two teams, red and blue, against each other in a shared simulated environment. In general, the objectives for each team are as follows: The blue team assets must navigate through hostile enemy environments, collecting intelligence and reporting back to base, while minimizing the losses. The red team assets must minimize the loss of intelligence to the blue team while maximizing blue team expenditure in fuel and assets. Scenarios range from an area of 25 km2 with 10 agents on each team, to 4000 km2 and >300 agents on each team.

Embry-Riddle Aeronautical University (ERAU) is working with the Air Force Research Lab (AFRL) to develop a distributed multi-layer autonomous UAS planning and control technology for gathering intelligence in Anti-Access Area Denial (A2/AD) environments populated by intelligent adaptive adversaries. These resilient autonomous systems are able to navigate through hostile environments while performing Intelligence, Surveillance, and Reconnaissance (ISR) tasks, and minimizing the loss of assets. Our approach incorporates artificial life concepts, with a high-level architecture divided into three biologically inspired layers: cyber-physical, reactive, and deliberative. Each layer has a dynamic level of influence over the behavior of the agent. Algorithms within the layers act on a filtered view of reality, abstracted in the layer immediately below. Each layer takes input from the layer below, provides output to the layer above, and provides direction to the layer below. Fast-reactive control systems in lower layers ensure a stable environment supporting cognitive function on higher layers. The cyber-physical layer represents the central nervous system of the individual, consisting of elements of the vehicle that cannot be changed such as sensors, power plant, and physical configuration. On the reactive layer, the system uses an artificial life paradigm, where each agent interacts with the environment using a set of simple rules regarding wants and needs. Information is communicated explicitly via message passing and implicitly via observation and recognition of behavior. In the deliberative layer, individual agents look outward to the group, deliberating on efficient resource management and cooperation with other agents. Strategies at all layers are developed using machine learning techniques such as Genetic Algorithm (GA) or NN applied to system training that takes place prior to the mission.

Microreactor concepts have seen increasing interest in recent years given their potential use for meeting energy needs in remote and grid-isolated communities. Given the small power outputs of microreactors and the potential for remote siting, there is a need to reduce operational staffing levels to improve their economic viability. A key enabling technology for this purpose is autonomous control that enables autonomous operation of microreactors. While several options for autonomous control exist, the level of human operator involvement in operational decision making needs to be defined as part of the system design process. Once this separation of function is achieved, several options exist for autonomous control ranging from simple automation of some procedures to fully autonomous operational mode decision making and execution.A critical aspect of autonomous decision making is the ability to have as complete an awareness of the system state as possible. In addition to data on temperature, pressure, flow, and neutron flux, measurements of the condition of important components will be necessary. This type of information will allow the autonomous control system to adapt its decision making to any changing conditions within the reactor, thereby not compromising safety while continuing to operate for as long as possible. Several technical advances are needed before widespread use of autonomous control in microreactors. These include sensor and instrumentation technologies that are capable of long term, unattended operation in harsh environments, technologies for inferring the state of the microreactor system or subsystems in an automated fashion, algorithms for predictive decision making that account for the assessed condition of the microreactor subsystems as well as the potential impact on those components of any operational decision, and actuators and control system hardware that are also long-lived in a harsh environment. In addition, given the need to remotely monitor the operations, cybersecurity requirements will likely need to be imposed to ensure secure operations.

The development of autonomous planning and control system software often results in a custom design concept and software specific to a particular control application. This paper describes a software framework for orchestrating the planning and execution of autonomous activities of an unmanned vehicle, or a group of cooperating vehicles, that can apply to a wide range of autonomy applications. The framework supports an arbitrary span of autonomous capability, ranging from simple low level tasking, requiring much human intervention, to higher level mission-oriented tasking, requiring much less. The approach integrates the four basic functions of all intelligent devises or agents (plan development, plan monitoring, plan diagnosing, and plan execution), with the mathematical discipline of hierarchical planning and control. The result is a domain-independent software framework, to which domain-dependent modules for planning, monitoring, and diagnosing are easily added. This framework for autonomy, combined with the requisite logic for vehicle control, can then be deployed to realize the desired level of autonomous vehicle operation.

Autonomous vehicles, e.g., cars, aircraft or ships, will need to accept some degree of human control for the coming years. Consequently, a method of controlling autonomous systems (ASs) that integrates control inputs from humans and machines is critical. We describe a framework for blended autonomy, in which humans and ASs interact with varying degrees of control to safely achieve a task. We empirically compare collaborative control tasks in which the human and AS have identical or conflicting objectives, under three main control frameworks: (1) leader-follower control (based on Stackelberg games); (2) blended control; and (3) switching control. We validate our results on a car steering control model, given communication delays, noise and different collaboration levels.

Aiming at the problems of the existing field test for DC charging pile of electric vehicles, such as tedious preparation and complex operation process, a modular DC charging pile test device is developed. Based on the charging requirement and working principle, five modular which are power module, measuring module, communication module, control module and the central processing unit are configured, the test device of compact makeup and modular connection is achieved. The test prototype based on the above modular design is assembled and applied to the field test of DC charging pile. The test results show that the device has stable communication and reliable operation ability and the test accuracy of electrical parameters meets the design requirements. The achievement in this paper can provide important technical support for modular industrial assembly design, commercial and industrial application of DC charging pile in field test.KeywordsElectric vehicle (EV)DC ChargingModular designField test

Knowledge and skills related to scientific thinking, practices and communication are essential, but they are rarely taught explicitly together in undergraduate research training. A course entitled “LSM3201 Research and Communication in Life Sciences” was introduced to teach these principles and skills explicitly to students engaged in ongoing research, hence combining course instructions to the process of scientific inquiry, in order to enhance their confidence and abilities in research and communication. This study was conducted to determine the impact of this course on students who took the course (the experimental group) by comparing with those who did not (the control group) through a quasi-experimental design of pre- and post- surveys as well as tests. The study clearly found that students’ confidence in their knowledge of and abilities in research and communication improved significantly after they had taken the course. The improvements were significantly greater in the experimental group than the control group, and were consistent with significant gains in specific knowledge. The control group showed minimal improvements in perception and levels of confidence in their abilities within the same duration of the study. The course was considered to have a medium to large impact on many of the items related to measuring levels of scientific thinking, practices and communication within the process of scientific inquiry. By concurrently providing instructions on scientific thinking, practices and communication to students engaged in ongoing research, the findings indicate that the course— in its design, delivery and assessment—has managed to enhance students’ confidence in their knowledge and abilities in research and communication.

In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently and effectively. In XAR-Miner, raw XML data are first transform ed to either an Indexed Content Tree (IX-tree) or M ulti-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection in the AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized meta-patterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent under-generalization or over-generalization. Resultant generalized meta-patterns are used to generate large ARs that meet the support and confidence levels. An efficient AR mining algorithm is also presented based on candidate AR generation in the hierarchy of generalized meta-patterns. The experiments show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML docume nts than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

The fourth industrial revolution (Industry 4.0) has reformed manufacturing through the integration of artificial intelligence (AI), Internet of Things (IoT), big data analytics, cloud computing and advanced robotics, 5G connectivity, and emerging technologies like quantum computing and augmented reality (AR). Smart factories, characterized by interconnected, data-driven ecosystems, optimize operational efficiency, reduce downtime, enhance sustainability, and enable autonomous production systems. This paper provides an expansive, multidimensional analysis of AI-driven smart factories, focusing on the evolution from predictive maintenance (PdM) to fully autonomous production systems. By synthesizing advancements in machine learning (ML), IoT, digital twins, generative AI, edge computing, 5G, and emerging technologies, the study evaluates their impact on efficiency, cost reduction, sustainability, workforce dynamics, ethical considerations, and regulatory compliance. Key challenges, including data quality, AI explain ability, system integration, cybersecurity, workforce reskilling, and regulatory frameworks, are thoroughly examined, alongside opportunities for innovation. Findings demonstrate that PdM achieves up to 97.3% accuracy in failure prediction, reducing downtime by 50% and costs by 10–40%, while autonomous systems, exemplified by Tesla’s Gigafactory, boost throughput by 30%. Future directions, including explainable AI (XAI), federated learning, self-aware assets, human-AI collaboration, and quantum computing, are proposed to foster resilient, sustainable, and inclusive manufacturing ecosystems.