Integrated System Research Articles

Residential medication management reviews: a call for improved collaboration and systems integration in aged care

Purpose Residential Medication Management Reviews (RMMR) are a key component of safe, high-quality aged care service delivery. The RMMR is an interdisciplinary approach to medication management that relies on collaboration, communication and integration of systems. Acting as an improvement opportunity, the purpose of this paper is to present findings from a regional study, aimed at assessing the impact of RMMR on falls and hospital admissions and identifies barriers to impactful research in this area. Design/methodology/approach A three-year retrospective quantitative longitudinal practice audit was conducted in a regional state-managed high-care residential facility in Queensland, Australia. Data was collected pre-RMMR and six months post-RMMR. Findings The data collected was insufficient to achieve statistical significance or demonstrate the impact of RMMR on health outcomes. Factors impeding the research included limited ability to collect the data due to the lack of integration of information systems and limited stakeholder engagement resulting in poor recruitment. This highlights the need for improvement in RMMR processes and practices, and indeed wider research collaboration in the aged care sector. Research limitations/implications Thís paper acts as a call to action to improve research and interprofessional collaboration in Australian aged care. Originality/value The aged care industry needs high-quality research to drive practice improvement and collaborative care and service delivery. This paper advocates for improvements in the aged care sector with respect to research engagement and communication pathways between service providers. Advancement in systems integration for information sharing, recruitment of participants and stakeholder engagement will support evidence-based practice and process change.

Correlation-based polarity-check algorithm for instrument transformers

A polarity identification is very important for operation of transformers, measurement and protection equipment, where it is useful in analyzing of transformer connections and operation as well as testing of protective systems. Moreover, it’s essential in assessment of power systems performance during both normal and abnormal operation. Ensuring the correct polarity of the primary and secondary windings in voltage and current transformers is of paramount importance for various measurement and protection schemes in power networks. This paper proposes a digital polarity detector and tester using correlation coefficients and nine polarity indices calculated for instrument transformer signals. In order to test the performance of the proposed polarity tester algorithm, MATLAB code is imported to the LABVIEW model, and the numerical data obtained from the synchronous generator terminals via instrument transformers are interfaced with the computer through the Data Acquisition Card (DAC). The experimental system consists of a motor-generator set supplying a three-phase inductive load with instrument transformers connected to measure each phase voltage and current. The obtained results for various operating conditions and different types of abnormal conditions prove that the suggested algorithm is accurate, reliable and applicable to smart grids and substation automation systems. It can be considered as an integrated system incorporated with digital fault recorders, relays and meters.

Centralized Platform for Streamlining Maharashtra Tourism

This paper introduces a centralized travel planner platform specifically designed to cater to tourists exploring Maharashtra's forts, temples, and beaches. The platform offers personalized travel packages tailored to individual user preferences, featuring options such as guide assignments, secure travel routes, recommendations for nearby restaurants and accommodations, and notable attractions in the vicinity. It addresses the challenges posed by the fragmented nature of current travel planning resources, particularly for those interested in Maharashtra’s rich cultural and natural landmarks. By incorporating a recommendation system, customizable packages, and real-time data integration, the platform provides a seamless and comprehensive travel planning experience. Preliminary tests suggest that the platform significantly improves the user experience by simplifying the process of organizing trips. Key Words: Maharashtra tourism, local travel planning, forts, temples, beaches, personalized recommendations.

Enhancing reflective practices in higher education with AI-supported anthropomorphic coaches

This paper reports on a pilot study exploring the use of generative AI and anthropomorphic coaches to enhance students’ reflective practices. Reflective practice is crucial for critical thinking and self-awareness. AI-driven feedback systems and lifelike avatars may create interactive, engaging environments for deeper student reflection. Drawing on observations from an ongoing project using Soul Machines Studio at the University of Auckland Business School, this inquiry considers anthropomorphic AI coaches’ ability to provide timely feedback and support reflective work in postgraduate courses. As a work-in-progress, we describe two use cases: (a) Refining team collaboration through AI-guided reflection and (b) providing feedback on reflective journal writing. We also outline future work on scalability, longitudinal studies, learning management system integration, cross-cultural adaptation, and ethical frameworks. We highlight AI's potential to develop students’ reflective practices and contribute to integrating advanced technologies in education.

Revolutionizing Sports Education using AI & ML

In the realm of sports education and performance analysis, there exists a pressing need to overcome barriers such as geographical limitations, cost constraints, and time constraints inherent in traditional teaching methodologies. In response to these challenges, this paper presents a novel sports education system leveraging artificial intelligence (AI) technologies. This allinclusive system allows for easy access to sports academies and training facilities based on geographic proximity by introducing an innovative teaching platform driven by AI and GPS integration. Moreover, it offers an extensive library of sports laws, sophisticated strategies, and professional advice, encouraging ongoing skill development. Setting goals and closely observing performance are made easier by the system's integration of AI-driven forecasts, such as victory probabilities. The central aim of this initiative is to redefine the landscape of sports education by offering a convenient, cost-effective, and highly efficient solution accessible to individuals at all proficiency levels. In addition to its educational advantages, the system is poised to make significant contributions to sports analytics, supplying extensive data and insights for both researchers and practitioners. This proposal advocates a progressive approach that moves beyond traditional methods, heralding a new era in sports education and performance analytics

Agreeing to Disagree: Translating Representations to Uncover a Unified Representation for Social Robot Actions

Researchers and designers of social robots often approach robot control system design from a single perspective; such as designing autonomous robots, teleoperated robots, or robots programmed by an end-user. While each design approach presents a tradeoff between some advantages and limitations, there is an opportunity to integrate these approaches where people benefit from the best-fit approach for their use case. In this work, we propose integrating these seemingly distinct robot control approaches to uncover a common data representation of social actions defining social expression by a robot. We demonstrate the value of integrating an authoring system, teleoperation interface, and robot planning system by integrating instances of these systems for robot storytelling. By relying on an integrated system, domain experts can define behaviors through end-user interfaces that teleoperators and autonomous robot programmers can use directly thus providing a cohesive expert-driven robot system.

MeDi‐TODER: Medical Domain‐Incremental Task‐Oriented Dialogue Generator Using Experience Replay

ABSTRACTArtificial intelligence (AI) technology has brought groundbreaking changes to the healthcare domain. Specifically, the integration of a medical dialogue system (MDS) has facilitated interactions with patients, identifying meaningful information such as symptoms and medications from their dialogue history to generate appropriate responses. However, shortcomings arise when MDS lacks access to the patient's cumulative history or prior domain knowledge, resulting in the generation of inaccurate responses. To address this challenge, we propose a medical domain‐incremental task‐oriented dialogue generator using experience replay (MeDi‐TODER) that applies the continual learning technique to the medical task‐oriented dialogue generator. By strategically sampling and storing exemplars from previous domains and rehearsing it as it learns, the model effectively retains knowledge and can respond to the novel domains. Extensive experiments demonstrated that MeDi‐TODER significantly outperforms other models that lack continual learning in both natural language generation and natural language understanding. Notably, our proposed method achieves the highest scores, surpassing the upper‐class benchmarks.

Analysis the Principle and Applications for Locomotive Engine

Abstract. The evolution of locomotives is the cornerstone of modern transport and has had a profound impact on economic growth and social mobility. From the pioneering steam locomotives of the 19th century, which marked the beginning of a transformative journey for rail transport, to today's complex diesel and electric locomotives, each technological advance has shaped the efficiency, speed and environmental impact of rail systems. With this in mind, this study will offer a systematically analysis for the principle as well as applications for such engine. Steam locomotives revolutionized the transportation of goods and passengers, laying the foundation for the future of rail travel. Diesel locomotives further enhance operational flexibility and reliability, while electric locomotives are becoming mainstream due to their energy efficiency and low emissions. This study explores the historical development of locomotives, highlighting key technological milestones and their impact on rail transport. Meanwhile, the current advances in locomotive technology, including the integration of hybrid power systems and alternative energy sources, are studied. As the rail industry faces increasing pressure for sustainability, it is important to understand the evolution and future prospects of locomotives. By analyzing these developments, this paper aims to provide insights into different locomotive principles and how locomotive technology is evolving in an era of increased environmental awareness and technological innovation, shaping the future of rail transportation.

Vascular Extracellular Matrix in Atherosclerosis

The extracellular matrix (ECM) plays a central role in the structural integrity and functionality of the cardiovascular system. Moreover, the ECM is involved in atherosclerotic plaque formation and stability. In fact, ECM remodeling affects plaque stability, cellular migration, and inflammatory responses. Collagens, fibronectin, laminin, elastin, and proteoglycans are crucial proteins during atherosclerosis development. This dynamic remodeling is driven by proteolytic enzymes such as matrix metalloproteinases (MMPs), cathepsins, and serine proteases. Exploring and investigating ECM dynamics is an important step to designing innovative therapeutic strategies targeting ECM remodeling mechanisms, thus offering significant advantages in the management of cardiovascular diseases. This review illustrates the structure and role of vascular ECM, presenting a new perspective on ECM remodeling and its potential as a therapeutic target in atherosclerosis treatments.

A Comprehensive Review of Artificial Intelligence and Machine Learning in Control Theory

Abstract: Traditional control methods, such as proportional-integral-derivative (PID) controllers and linear-quadratic regulators (LQRs), have proven effective for linear and well-modeled systems. However, these methods often perform poorly in nonlinear, complex and dynamic environments. The paper aims to investigate the modern control systems by integrating artificial intelligence (AI) techniques, such as machine learning (ML), reinforcement learning (RL), deep learning, and fuzzy logic, to enhance their adaptive, robust, and predictive capabilities. And it reviews the literature and analyzes AI integration in control systems. The proposed strategies include supervised learning for trajectory optimization and fault detection, reinforcement learning for optimal control in dynamic environments, neural networks for complex nonlinear function approximation, and fuzzy logic for handling uncertainty and imprecise inputs. AI techniques significantly enhance the ability to tackle nonlinear problems and dynamic changes, demonstrating superior performance in applications like self-driving cars adapting to various road conditions and optimal energy distribution in smart grids. Despite the challenges of computational complexity, scalability, and the safety and reliability in the implementation of interpretable AI models, this paper suggests that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.

Modular design and custom development in ERP systems

Abstract. Enterprise Resource Planning (ERP) systems are integral to modern business operations, offering streamlined processes and enhanced efficiency. This paper explores the advantages and disadvantages of modular design in ERP systems and examines custom development methods to address unique industry requirements. The study highlights the benefits of modular design, such as flexibility, scalability, and reduced complexity, while also addressing challenges like integration issues and system fragmentation. Strategies for efficient module integration, including standardized protocols and middleware solutions, are discussed to mitigate these challenges. Custom development methods, including vendor tools, scratch-built modules, and third-party collaborations, are analyzed for their effectiveness in achieving tailored ERP solutions. Case studies from the manufacturing, healthcare, and retail industries illustrate the practical applications and outcomes of modular ERP systems, providing insights into the implementation process and potential obstacles. The findings underscore the importance of careful planning, stakeholder involvement, and comprehensive training to ensure successful ERP system deployment and utilization.

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

On the use of synthetic data for body detection in maritime search and rescue operations

Time is a critical factor in maritime Search And Rescue (SAR) missions, during which promptly locating survivors is paramount. Unmanned Aerial Vehicles (UAVs) are a useful tool with which to increase the success rate by rapidly identifying targets. While this task can be performed using other means, such as helicopters, the cost-effectiveness of UAVs makes them an effective choice. Moreover, these vehicles allow the easy integration of automatic systems that can be used to assist in the search process. Despite the impact of artificial intelligence on autonomous technology, there are still two major drawbacks to overcome: the need for sufficient training data to cover the wide variability of scenes that a UAV may encounter and the strong dependence of the generated models on the specific characteristics of the training samples. In this work, we address these challenges by proposing a novel approach that leverages computer-generated synthetic data alongside novel modifications to the You Only Look Once (YOLO) architecture that enhance its robustness, adaptability to new environments, and accuracy in detecting small targets. Our method introduces a new patch-sample extraction technique and task-specific data augmentation, ensuring robust performance across diverse weather conditions. The results demonstrate our proposal’s superiority, showing an average 28% relative improvement in mean Average Precision (mAP) over the best-performing state-of-the-art baseline under training conditions with sufficient real data, and a remarkable 218% improvement when real data is limited. The proposal also presents a favorable balance between efficiency, effectiveness, and resource requirements.

A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data.

Agriculture is confronted with several challenges such as climate change, the loss of biodiversity and stagnating productivity. The massive increasing amount of data and new digital technologies promise to overcome them, but they necessitate careful data integration and data management to make them usable. The FAIRagro consortium is part of the National Research Data Infrastructure (NFDI) in Germany and will develop FAIR compliant infrastructure services for the agrosystems science community, which will be integrated in the existing research data infrastructure service landscape. Here we present the initial steps of designing and implementing the FAIRagro middleware infrastructure to connect existing data infrastructures. The middleware will feature services for the seamless data integration across diverse infrastructures. Data and metadata are streamlined for research in agrosystems science by downstream processing in the central FAIRagro Search and Inventory Portal and the data integration and analysis workflow system "SciWIn".

Evaluating the Preparedness of Freight Logistics Firms for Cyber-Physical Systems Integration: A SAP-LAP Methodology for Sustainable Development

Evaluating the Preparedness of Freight Logistics Firms for Cyber-Physical Systems Integration: A SAP-LAP Methodology for Sustainable Development

Chaos-based coverage path planning framework for mobile robots and its digital signal processing implementation

Abstract For special coverage path planning, chaos-based mobile robots present a promising solution. The robot can cover the area of interest rapidly with unpredictable trajectories. However, this unpredictability causes difficulties in trajectory control and the robot often makes sharp turns, affecting motion stability. To address these challenges, this paper proposes a universal chaos-based path planning framework for mobile robots. Firstly, a chaotic system that can generate multi-scroll and multi-wing attractors is designed to verify the effectiveness of the framework. Secondly, an integrated system consisting of mobile robots and chaos is proposed to generate chaotic trajectories. By the control parameters in the integrated system, the formation mechanism of continuous chaotic trajectories is shown, which makes the chaotic trajectory have the advantages of scaling, rotating, straightening, and bending, and the path planning no longer relies on the chaotic system only. Then, a regional path planning strategy is proposed to control the angular velocity and workspace of the robot, making its motion smooth and reducing trajectory redundancy. Experiments conducted under this framework have demonstrated its university, with various chaotic systems capable of generating efficient robot trajectories with high coverage rate. Compared with the state-of-the-art similar memoryless algorithms, the coverage task can be accomplished quickly and efficiently by our approach. Finally, the robot trajectory tracking control verifies the controllability of chaotic trajectories, and the implementation of chaotic trajectories with digital signal processing techniques further validates the feasibility of path planning frameworks.

Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes a significant workload on physicians, leading to a high risk of missed diagnoses. This study aims to develop an artificial intelligence (AI) model and application based on convolutional neural networks that can automatically recognize various lesions in small bowel capsule endoscopy. Three small bowel capsule endoscopy datasets were used for AI model training, validation, and testing, encompassing 12 categories of images. The model's performance was evaluated using metrics such as AUC, sensitivity, specificity, precision, accuracy, and F1 score to select the best model. A human-machine comparison experiment was conducted using the best model and endoscopists with varying levels of experience. Model interpretability was analyzed using Grad-CAM and SHAP techniques. Finally, a clinical application was developed based on the best model using PyQt5 technology. A total of 34,303 images were included in this study. The best model, MobileNetv3-large, achieved a weighted average sensitivity of 87.17%, specificity of 98.77%, and an AUC of 0.9897 across all categories. The application developed based on this model performed exceptionally well in comparison with endoscopists, achieving an accuracy of 87.17% and a processing speed of 75.04 frames per second, surpassing endoscopists of varying experience levels. The AI model and application developed based on convolutional neural networks can quickly and accurately identify 12 types of small bowel lesions. With its high sensitivity, this system can effectively assist physicians in interpreting small bowel capsule endoscopy images.Future studies will validate the AI system for video evaluations and real-world clinical integration.

Simulation framework and development of the Future Systems Simulator

Abstract The Aerospace Integration Research Centre (AIRC) at Cranfield University offers industry and academia an open environment to explore the opportunities for efficient integration of aircraft systems. As a part of the centre, Cranfield University, Rolls-Royce, and DCA Design International jointly have developed the Future Systems Simulator (FSS) for the purpose of research and development in areas such as human factors in aviation, single-pilot operations, future cockpit design, aircraft electrification, and alternative control approaches. Utilising the state-of-the-art modularity principles in simulation technology, the FSS is built to simulate a diverse range of current and novel aircraft, enabling researchers and industry partners to conduct experiments rapidly and efficiently. Central to the requirement, a unique, user-experience-centred development and design process is implemented for the development of the FSS. This paper presents the development process of such a flight simulator with an innovative flight deck. Furthermore, the paper demonstrates the FSS’s capabilities through case studies. The cutting-edge versatility and flexibility of the FSS are demonstrated through the diverse example research case studies. In the final section, the authors provide guidance for the development of an engineering flight simulator based on lessons learned in this project.

The system of support for medical decision-making in the formation of a rehabilitation diagnosis in the categories of the International Classification of Functioning, Disability and Health

Introduction. Comprehensive rehabilitation of patients is a key task of the national healthcare system. To create an effective rehabilitation program, it is necessary to establish an accurate rehabilitation diagnosis. Designing an individual medical rehabilitation plan based on a biopsychosocial approach, the practical tool of which was the “International Classification of Functioning, Disability and Health” (ICF), has changed the global concept of rehabilitation. A priority in the field of digital healthcare is the introduction of medical decision support systems, one of the tasks of which is to help in diagnosis and minimize medical errors aimed at improving the quality of medical care. The purpose of the work: to create a software system for making medical decisions in the formation of a rehabilitation diagnosis in the ICF categories. Materials and methods. content analysis was used to study and analyze medical decision-making systems (foreign and domestic) and the ICF; scientific and methodological work was carried out on the development of software for medical decision-making systems. Results. based on the work carried out, software has been developed to automate, systematize, and optimize the process of establishing a rehabilitation diagnosis in patients with ischemic stroke, standardize approaches to individualizing diagnosis formulation taking into account the severity of functional disorders. Research limitations. During the development and description of the software, the capabilities of medical information systems applied in the healthcare of Moscow were used. Conclusion. The biopsychosocial approach is one of the main principles of modern rehabilitation. Digital healthcare facilitates the integration of decision support systems that improve the quality of medical care. The implementation of the developed program into digital platforms of the healthcare system will reduce time costs and unify the procedure for making a rehabilitation diagnosis and all related processes, consistently preserving the necessary information about the patient.

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

AbstractSmart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.