Integrated System Research Articles

Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments

With the emergence of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the flow of data across the world is experiencing a rapid expansion. Unfortunately, this exponential growth is accompanied by a proportional increase in cyber threats, jeopardizing the security and integrity of computer systems. In this context, intrusion detection becomes a necessity to protect networks and systems against potential attacks, ensuring their proper functioning and reliability. In this paper, we propose a deep learning-based model for attack detection. This model utilizes a convolutional neural network to train the datasets, which are first cleaned and preprocessed. The model inputs are selected using an optimization method called adaptive large neighborhood search. The results obtained for the four datasets used – CICIDS2017, Edge-IIoTset, ToN-IoT windows7, and ToN-IoT windows10 – demonstrate the model’s effectiveness for both multi-class and binary classification cases. In the binary case, the accuracy reaches 99.85%, 100%, 99.97%, and 100%, respectively, and in the multi-class case, it stands at 99.81%, 94.98%, 99.92%, and 99.84%, respectively.

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.

Thermoelectric energy harvesting from geothermal micro-seepage

The field of thermoelectric energy harvesting has gained significant attention as a sustainable method to convert waste heat into useable electricity. This assessment focuses on the recent advancements in thermoelectric materials and their application in harvesting energy from geothermal micro-seepage a relatively untapped but promising source of renewable energy. We systematically analyze recent studies to assess the performance of various thermoelectric materials under geothermal conditions, evaluate the integration of thermoelectric systems with geothermal sources, and identify the key challenges and research gaps that persist in this domain. The paper aims to provide a roadmap for future research, highlighting the potential of thermoelectric technology to contribute to sustainable energy solutions while addressing the limitations that must be overcome for large-scale deployment.

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.

Research on the transmission of the Open University of Japan courses via multi-device apps oriented to the learning ecosystem

PurposeThe purpose of this paper is to propose the need for an integrated system for the production, authoring, addition and revision of webcast content and a multi-device apps mechanism with rights management for Internet transmission in the perspective of the OUJ course reuse.Design/methodology/approachThis paper designs systems that reuse the existing OUJ courses from the viewpoint of effective utilization, combine broadcasting lectures and online lectures of the OUJ courses and seamlessly link them as media-mix contents. In contrast to content development methodologies that assume embedded content production, this paper’s methodology takes an approach to producing and authoring webcasting content, flexibly adding and revising the content and transmitting that content over the internet via multi-device apps, taking rights management into account.FindingsThere is concern about the ELSI arising from the use of generative AI. It is directed at web content. This paper can contribute to resolving the ELSI surrounding the use of generative AI because it discusses a method of learning content development that clarifies the attribution of rights to content.Originality/valueThe originality of this paper lies in the fact that it is oriented to reuse existing lectures of the OUJ courses and simplify the rights clearance for them, from the perspective of the learning ecosystem. The value of this paper is that it can also serve as a technical and legal response to the production, authoring and Internet transmission of open university courses in the era of generative AI.

Bridging complexity through integrative systems neuroscience

Bridging complexity through integrative systems neuroscience

Large Language Model Agents as Prognostics and Health Management Copilots

Amid concerns of an aging or diminishing industrial workforce, the recent advancement of large language models (LLMs) presents an opportunity to alleviate potential experience gaps. In this context, we present a practical Prognostics and Health Management (PHM) workflow and self-evaluation framework that leverages LLMs as specialized in-the-loop agents to enhance operational efficiency without subverting human subject matter expertise. Specifically, we automate maintenance recommendations triggered by PHM alerts for monitoring the health of physical assets, using LLM agents to execute structured components of the standard maintenance recommendation protocol, including data processing, failure mode discovery, and evaluation. To illustrate this framework, we provide a case study based on historical data derived from PHM model alerts. We discuss requirements for the design and evaluation of such “PHM Copilots” and formalize key considerations for integrating LLMs into industrial domain applications. Refined deployment of our proposed end-to-end integrated system may enable less experienced and professionals to back-fill existing personnel at reduced costs.

Navigating the legal uncertainty around the governance of an integrated energy system using the values of the energy transition and energy justice

AbstractIn order to realise the European Union's (EU) transition to clean and carbon‐neutral energy systems, various individual infrastructure systems and energy networks will have to coordinate and operate as an integrated energy system. Traditionally, these different infrastructure systems operate within their own sectors and are governed by separate legal regimes. The intersection of these different legal rules and interests creates regulatory uncertainty and risk for infrastructure operators and can dissuade operators from participating in system integration. This paper explores how infrastructure operators can overcome the legal uncertainty and regulatory risks of operating as an integrated system, whilst also ensuring that the policy objectives of the energy transition are realised. This paper argues that, by pursuing public values and energy justice principles, infrastructure operators will overcome the legal uncertainty surrounding system integration. By doing so, infrastructure operators will facilitate the realisation of an energy transition that is just, inclusive and in the public interest.

Liouville-type theorem for higher order Hardy-Hénon type systems on the sphere

In this paper, we study Liouville type theorems for the positive solutions to the following higher order Hardy-Hénon type system involving the conformal GJMS operator on the sphere Sn. In order to study this we first employ the Mobius transform to transform the above Hardy-Hénon type system on the sphere Sn into a higher order elliptic system on Rn. Then, we show that every positive solution of the higher order elliptic system on Rn is a solution to the associated integral system on Rn by using polyharmonic average and iteration arguments. We use the method of moving planes in integral form to prove that there are no positive solutions for the integral system on Rn. Finally, together with the symmetry of the sphere Sn, we obtain the Liouville type theorem of the higher order Hardy-Hénon type system involving the GJMS operator on the sphere. The results of this paper are also new even for the Lane-Emden system on the sphere.

A novel integrated solar-driven system with Ag@SiO2 nanofluid filters for generating hydrogen using seawater: Parametric assessment and optimization

Both renewable energy and fresh water inputs are required to develop sustainable green hydrogen. However, there is still a lack of an integrated system generating hydrogen cleanly. Therefore, this study aims to develop a full spectrum solar energy-driven system to co-generate fresh water and hydrogen. The seawater desalination and proton exchange membrane (PEM) electrolysis are introduced into a Ag@SiO2 nanofluid-filtered photovoltaic/thermal (PV/T) system. The desalinated seawater can be directly used for the electrolysis, realizing the self-supply of water for the hydrogen production process. A comprehensive examination is performed via modelling the integrated system to assess the influences of mass flow rates and nanofluid parameters on system function. Results reveal that the system performance is more sensitive to the mass flow rate of nanofluid than that of coolant, and concentrated nanofluids can easily affect system performance by adjusting their depth. Furthermore, mass flow rates and nanofluid parameters are optimized using a genetic algorithm with multiple-objectives. It was determined that the direct contact membrane distillation (DCMD) unit is capable of producing sufficient water for the electrolysis process. The optimal system attains a hydrogen production rate of 1.95 × 10−5 kg/s, a specific thermal energy consumption of 310.18 kWh/m3, and a system efficiency of 22.39%.

Enhancing the Cultivation of Moina sp.: An Integration of Aeration System with Green Water Detection and pH Monitoring

Enhancing the Cultivation of Moina sp.: An Integration of Aeration System with Green Water Detection and pH Monitoring

3-D impedance matching network (IMN) based on through-silicon via (TSV) for RF energy harvesting system

Radio Frequency Energy Harvesting (RFEH) system can harvest radio frequency (RF) energy from the natural environment, showing potential applications to play the role of the battery in specific settings, such as outdoors or human body, demonstrating vast potential applications on the scenarios with difficulty to renew the traditional battery. In the RFEH system, Impedance Matching Network (IMN) consumes substantial amounts of metal wire and chip area in standard CMOS process and is not easy to be integrated into the monolithic substrate. The planar structure shows the shortcoming of large area and prominent parasitic influences, limiting its integration in the REFH system. The development of 3-D integrated circuit technology based on Through-Silicon Vias (TSV) offers a possible solution for RF passive devices. In the paper, a novel TSV-based 3-D IMN is designed. The capacitor is implemented with the TSV structure, while the inductor is designed with Redistribution Layer (RDL) spiral pattern. The co-design of 3D LC matching circuit shows the merits of the proposed solution, including high performance and compact size. It can be used as a technical solution for the integration of IMN in a monolithic CMOS RFEH system.

Parametric Optimization Approach to Evaluate Dynamic Shading Within Double-Skin Insulated Glazed Units for Multi-Criteria Daylighting Performance in Tropics

This research aims to support the choice of an appropriate dynamic louver shading system (DL-SS) within double-skin facade insulated glazed units (DSF-IGUs) as a high-performance integrated window system (DSF-IGUs/DL-SS) that meets both thermal and energy performance via daylight availability under a tropical climate. The research framework has developed a multi-objective optimization method to achieve research objectives via optimizing two different scenarios of the proposed system. The first scenario was optimized for daylighting availability, meanwhile, the second scenario was optimized for energy and thermal performance. For each scenario, the best solutions are selected from respective Pareto fronts according to energy efficiency criteria, thermal comfort via enhancing daylighting availability. Based on the best options resulting from both optimizations, the final step involved comparing the results of all performance indicators in the best cases to select the best solution. Overall, based on the optimizing objectives, the ranking of the best cases varied based on giving priority to the improvement objective in the optimization process. For each scenario, the best solutions are selected from the respective Pareto fronts. Overall, ranking of the best cases varied based on giving priority to the improvement objectives. Optimizing DL-SS within DSF-IGUs while giving priority to improving energy and thermal comfort while maintaining daylighting at acceptable levels is more reasonable. Thus, the DSF-IGUs/DL-SS best-case resulting from the second optimization scenario was overcome all best cases and ranked first in energy and thermal comfort. Compared to the base case, the differences of total Predicted mean vote and percentage of dissatisfied for better thermal comfort achieved were -0.35% and -1.48% with an average decreased by 22.99% and 28.72%, respectively. The differences of total energy and cooling load for better energy performance reduced by -96.84 kwh/m2. and -86.88 kwh with an average decreased by 25.33% and 26.20%, respectively. Meanwhile, the total satisfied of spatial Daylight Autonomy for better daylighting distribution and better daylighting availability of useful daylighting illuminance improvement were improved by -5.54% and +24.76% with an average percentage variation increased by 6.25% and 36.87%, respectively.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

Distribution dynamics of waterbirds in Dongting Lake and surrounding lakes

Abstract Dongting Lake and the lakes surrounding it are spatially relatively independent, yet they form an inseparable integrated system for waterbirds. Understanding the changes in waterbird distribution pattern in Dongting Lake and surrounding lakes is important for waterbird conservation and management. Based on six surveys during the winter period of waterbird and habitat data, we studied the changes in distribution pattern of different functional group of waterbirds between Dongting Lake and surrounding lakes and explored the effect of habitat area in Dongting Lake on waterbird distribution. The results revealed that the habitat areas within Dongting Lake are larger but less stable, whereas those in the surrounding lakes are much smaller but more stable. During regular hydrology periods, waterbirds were mainly distributed in Dongting Lake, whereas most of the diving ducks and vegetation gleaners occurred in surrounding lakes; during extreme dry periods, many waterbirds shifted to surrounding lakes (e.g., dabbling ducks and gulls). The Percentage Similarity Index (PSI) of these groups, which reflects waterbirds’ distribution dynamics, exhibited negative correlations with changes in water area within Dongting Lake. The percentages of these water-dependent groups and water area in Dongting Lake exhibited a positive correlation. Water area in Dongting Lake could reflect the distribution pattern of waterbirds between Dongting Lake and surrounding lakes. Our results emphasize the importance of the overall protection of wetlands in the Dongting Lake region.

Intelligent Optimization and Impact Analysis of Energy Efficiency and Carbon Reduction in the High-Temperature Sintered Ore Production Process

The coordinated optimization of energy conservation, efficiency improvement, and pollution reduction in the sintering production process is vital for the efficient and sustainable development of the sintering department. However, previous studies have shown shortcomings in the multi-objective collaborative optimization of sintering systems and the quantification of pollutant impacts. To address these, this paper proposes a multi-objective optimization method integrated with the NSGA-III algorithm and establishes an integrated system optimization model for sintered ore production and high-temperature waste heat recovery. The results demonstrate significant improvements: energy utilization efficiency increased by 0.67%, energy consumption decreased by 17.3 MJ/t, production costs were reduced by 11.45 CNY/t, and the emissions of CO2, SO2, and NOx were reduced by 0.464 kg/t, 0.034 kg/t, and 0.008 kg/t, respectively. Additionally, the study identified optimal configuration parameters and analyzed the quantitative impact of several key factors on multiple indicators. The results also show that reducing the water content of the mixture, decreasing the middling coal content in the fuel, and increasing the thickness of the material layer are effective strategies to reduce energy consumption and pollutant emissions in the sintering process. Overall, implementing these optimizations can bring significant economic and environmental benefits to the steel industry.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

A Real-Time Associative Feature-Based Customer Relationship Management and Enterprise Resource Planning Integration Model for Small- and Medium-Sized Enterprises

Abstract Customer relationship management (CRM) and enterprise resource planning (ERP) have been extensively discussed in the research literature respectively. However, existing studies do not reach a complete agreement on the CRM/ERP integration method, especially for small- and medium-sized enterprises (SMEs). Generally, this work proposed a novel method for CRM/ERP integration approach via associative feature technology. A new feature type, Real-time Data Link Board (RDLB), is developed and detailed as a generic information carrier solution for multisystem integration between CRM and ERP packages. Valuable data elements are mapped into such data boards dynamically and synchronized across different data sources, structures, and databases. The contribution of this work is to present a well-defined and generically reusable data carrier definition, alongside related methods for ensuring consistent data modeling during the system integration phase across various digitalization engineering implementation projects. With this approach, the consistent management of data integration across intricate systems is achievable throughout application lifecycles, supported by the object-oriented software engineering foundation.

Design of a Broadband and High-Isolation CMOS Active Quasi-Circulator

Using industry standard TSMC 90 nm CMOS technology, a broadband and high-isolation active quasi-circulator MMIC was fabricated to meet the needs of future communication system integration. An active balun and phase inverter stage made of a common-source (CS) field-effect transistor (FET) comprised the quasi-circulator that was suggested. A cascade CS stage was used to produce the active balun, which increased the isolation of |S12| and |S23| through the unilateral characteristic of the FET and allowed for transmission of |S21| and |S32|. Further, a CS FET connected in parallel to ports 1 and 3 for phase cancellation can effectively increase the quasi-circulator’s isolation |S31|. Theoretical analysis for the detailed circuit was shown in the main paper. The results show that all isolations are better than 20 dB over 5–33 GHz, and the measured findings reveal that the suggested quasi-circulator has an insertion loss of less than 10 dB. It is possible to reach an isolation level of 55 dB between ports 1 and 3, which is greater than present research.