Operational Domain Research Articles

Modelling Factors Influencing IoT Adoption: With a Focus on Agricultural Logistics Operations

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.

Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections

Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV’s automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV’s operational design domain and the development and optimization of the AV’s ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations.

Social navigation framework for autonomous vehicle with hierarchical cyber-physical system architecture

An autonomous vehicle operating alongside humans should ideally have a high social capability, such as being able to communicate with humans, negotiate space, predict reactions, etc. This can be achieved by a prediction feature trained with diverse data on human behavior. Hierarchical cyber-physical system (CPS) architecture design, which sends the prediction feature to an external server while observing a limited operational design domain (ODD) and acquiring data continuously, has great potential to refine the training process as well as improve the performance. However, this architecture design requires the planning and prediction modules to be explicitly decoupled, which takes away from the recent success on social navigation. In this paper, we propose a novel autonomous navigation framework enabling social behavior while decoupling the planning and prediction modules to take advantage of the hierarchical CPS architecture. In the proposed framework, pedestrian trajectories are predicted as reactions to pre-generated candidates for an ego vehicle trajectory, and the ego vehicle trajectory is then selected to maximize mutual benefit to both the ego vehicle and surrounding pedestrians. We evaluated the proposed framework with simulations using the social force model and found that it was able to achieve the social behavior.

Long plasma duration operation analyses with an international multi-machine (tokamaks and stellarators) database

Combined high-fusion performance and long-pulse operation is one of the key integration challenges for fusion energy development in magnetic devices. Addressing these challenges requires an integrated vision of physics and engineering aspects with the purpose of simultaneously increasing time duration and fusion performance. Significant progress has been made in tokamaks and stellarators, including very recent achievement in duration and/or performance. This progress is reviewed by analyzing the experimental data (109 plasma pulses with a total of 3200 data points, i.e. on average 29 data per pulse) provided by ten tokamaks (in alphabetical order: Axially Symmetric Divertor Experiment Upgrade, DIII-D, Experimental Advanced Superconducting Tokamak, Joint European Torus, JT-60 Upgrade, Korea Superconducting Tokamak Advanced Research, tokamak à configuration variable, Tokamak Fusion Test Reactor, Tore Supra, W Environment in Steady-State Tokamak) and two stellarators (Large Helical Device and Wendelstein 7-X) expanding the pioneering work of Kikuchi (Kikuchi M. and Azumi M. 2015 Frontiers in Fusion Research II: Introduction to Modern Tokamak Physics (Springer)). Data have been gathered up to January 2022 and coordination has been provided by the recently created International Energy Agency-International Atomic Energy Agency international Coordination on International Challenges on Long duration OPeration group. By exploiting the multi-machine international database, recent progress in terms of injected energies (e.g. 1730 MJ in L-mode, 425 MJ in H-mode), durations (1056 s in L-mode, 101 s in H-mode), injected powers, and sustained performance will be reviewed. Progress has been made to sustain long-pulse operation in tokamaks and stellarators with superconducting coils, actively cooled components, and/or with metallic walls. The graph of the fusion triple products as a function of duration shows a dramatic reduction of, at least two orders of magnitude when increasing the plasma duration from less than 1 s to 100 s. Indeed, long-pulse operation is usually reached in dominant electron-heating modes at reduced density (current drive optimization) but with low ion temperatures ranging from 1 to 3 keV for discharges above 100 s. Difficulties in extending the duration may arise from coupling high heating powers over long durations and the evolving plasma-wall interaction towards an unstable operational domain. Possible causes limiting the duration and critical issues to be addressed prior to ITER operation and DEMO design are reported and analyzed.

A real‐time critical‐scenario‐generation framework for defect detection of autonomous driving system

AbstractTo find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors’ approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations.

Boundary triples and Weyl functions for Dirac operators with singular interactions

In this paper, we develop a systematic approach to treat Dirac operators [Formula: see text] with singular electrostatic, Lorentz scalar, and anomalous magnetic interactions of strengths [Formula: see text], respectively, supported on points in [Formula: see text], curves in [Formula: see text], and surfaces in [Formula: see text] that is based on boundary triples and their associated Weyl functions. First, we discuss the one-dimensional case which also serves as a motivation for the multidimensional setting. Afterwards, in the two- and three-dimensional situation we construct quasi, generalized, and ordinary boundary triples and their Weyl functions, and provide a detailed characterization of the associated Sobolev spaces, trace theorems, and the mapping properties of integral operators which play an important role in the analysis of [Formula: see text]. We make a substantial step towards more rough interaction supports [Formula: see text] and consider general compact Lipschitz hypersurfaces. We derive conditions for the interaction strengths such that the operators [Formula: see text] are self-adjoint, obtain a Krein-type resolvent formula, and characterize the essential and discrete spectrum. These conditions include purely Lorentz scalar and purely non-critical anomalous magnetic interactions as well as the confinement case, the latter having an important application in the mathematical description of graphene. Using a certain ordinary boundary triple, we also show the self-adjointness of [Formula: see text] for arbitrary critical combinations of the interaction strengths under the condition that [Formula: see text] is [Formula: see text]-smooth and derive its spectral properties. In particular, in the critical case, a loss of Sobolev regularity in the operator domain and a possible additional point of the essential spectrum are observed.

Integrated oncology operational dashboard for clinical and operational quality improvement.

588 Background: MaineHealth Cancer Care Network, comprised of nine member and two affiliate cancer centers, provides care to more than 70% of analytic cancer cases in largely rural south-central Maine and eastern New Hampshire. To drive high quality and performance and harmonize care across the network, an integrated operational dashboard was created. Methods: A team of clinicians, operational leaders and business intelligence analysts identified improvement goals across clinical and operational domains. Using a Microsoft Power BI platform, an integrated dashboard with extracts from various non-interoperable data sources were migrated together to illustrate all relevant metrics. Divided into five pages, the Summary page allowed for a high level visualization of financial improvement metrics across the service line. The Growth page included trends of template utilization, provider access, encounter and financial statistics. The Service Excellence page measured diagnosis to treatment times, new patient volumes, timely chart completion rates and provider net promoter scores. Lastly, the Innovation and Transformation page measured clinical trial accruals, use of Epic TNM staging (Epic Systems, Verona, WI), Clinical Pathways (Elsevier, Amsterdam, NL) and use of NCCN distress thermometer for patient assessment. All data was searchable based on chosen fiscal years, practice locations and provider names with automated monthly data refreshes. Dashboard access to clinical and operational staff with monthly reviews drove performance improvement. Results: Since creation in January 2023, dashboard has been leveraged to improve structured Epic TNM staging by medical oncology providers from 46.9% in FY22 to 53.4% in FY23. Time to treatment data has helped identify process bottlenecks. Provider productivity metrics have been used to launch a compensation reform program incorporating production based incentives and improve patient access. Information transparency has improved team engagement and ownership. Conclusions: High quality cancer care delivery requires equal attention to clinical and operational metrics. In most organizations, this data is siloed across disparate software platforms with limited visibility and access to care team members, impeding quality initiatives. Implementation of the system-wide integrated quality dashboard has led to tangible improvements in care delivery and provider engagement. Additional work to incorporate provider well-being data is underway.

Intelligent fault diagnosis of hydroelectric units based on radar maps and improved GoogleNet by depthwise separate convolution

Abstract Fault diagnosis plays an essential role in maintaining the safe and stable operation of hydroelectric units. In this paper, an intelligent fault diagnosis method based on radar maps and improved GoogleNet by depthwise separate convolution (DSC) is proposed to address the problems of low recognition accuracy and weak computing speed of fault diagnosis models in the field of hydroelectric unit fault diagnosis at present. Firstly, a one-dimensional signal sequence is obtained and denoised. Secondly, five time-domain features are extracted and radar maps are plotted. Then, an improved GoogleNet intelligent fault diagnosis model based on DSC (DSC-GoogLeNet) is constructed for training and validation. To assess the effectiveness of the proposed model, two case studies are conducted using the simulated dataset of the rotor experimental bench and the actual measured dataset of a domestic hydroelectric power plant. The results demonstrate that the average recognition accuracy of the fault diagnosis method proposed in this paper is as high as 99.04% on the simulated dataset, and even though the recognition accuracy decreases on the actually measurement dataset, it still has a recognition rate of 98.79%. The fault diagnosis performance is better than the other types of comparison models. The results demonstrate that the proposed fault diagnosis method holds significant engineering applicability in the domain of safe operation of hydroelectric units. It effectively addresses the existing challenges in fault diagnosis within this field with accuracy, stability, and efficiency.

Associations of self-advocacy and perceptions of care coordination among rural patients with early-stage cancer.

329 Background: Effective care coordination (CC) is an essential component of high-quality, patient-centered cancer care; however, many rural cancer patients experience significant CC-related challenges. For rural patients, one’s ability to self-advocate for their cancer care-related needs may play an important role in CC. To date, little is known about self-advocacy and care coordination in rural cancer patients. In this study, we examined associations between patient-reports of self-advocacy and CC among rural patients with early-stage cancer. Methods: 31 patients with early-stage (I-III) cancer completed the self-advocacy and care coordination questionnaires in 2021-2022. Participants were receiving active treatment and residing in rural areas of Hawaii at time of survey completion. Levels of self-advocacy were measured using the adapted Female Self-Advocacy in Cancer Survivorship Scale (FSACS; Hagan et al., 2018). The FSACS assesses overall self-advocacy and includes three subscales, Informed Decision Making, Effecting Communication, and Connected Strength. The validated Care Coordination Instrument (CCI) was used to assess patients’ perceptions of CC. The CCI assesses overall (total) and three CC domains, Communication, Navigation, and Operational. Pearson’s correlation analyses were conducted to examine associations between self-advocacy and CC. Results: Mean age of participants was 58.6, and 64.5% were female. The most common cancer types were breast (41.9%) and GI (32.3%). Overall, the total self-advocacy scores were positively associated with the total CCI and Operational domain scores ( r = .39 and .41, p < .05). Informed Decision Making and Effective Communication subscale scores were also positively correlated with the total CCI and CC domain scores ( r = .682-.945, p < .001). Connected Strength subscale scores were not associated with CCI scores. The presence of a patient navigator was associated with higher self-advocacy scores ( p = .011). Conclusions: Our findings indicate that higher levels of self-advocacy are associated with better perceptions of care coordination among rural patients with early-stage cancer. Conversely, patients with lower levels of self-advocacy report poorer CC and are less likely to present with a patient navigator. These findings suggest that interventions to enhance self-advocacy skills may be a potential strategy to improve rural patients’ CC experiences.

Enhancing Autonomous Vehicle Safety in Cold Climates by Using a Road Weather Model: Safely Avoiding Unnecessary Operational Design Domain Exits

<div>This study investigates the use of a road weather model (RWM) as a virtual sensing technique to assist autonomous vehicles (AVs) in driving safely, even in challenging winter weather conditions. In particular, we investigate how the AVs can remain within their operational design domain (ODD) for a greater duration and minimize unnecessary exits. As the road surface temperature (RST) is one of the most critical variables for driving safety in winter weather, we explore the use of the vehicle’s air temperature (AT) sensor as an indicator of RST. Data from both Road Weather Information System (RWIS) stations and vehicles measuring AT and road conditions were used. Results showed that using only the AT sensor as an indicator of RST could result in a high number of false warnings, but the accuracy improved significantly with the use of an RWM to model the RST. ROC-curve analysis resulted in an AUC value of 0.917 with the AT sensor and 0.985 with the RWM, while the true positive rate increased from 67% to 94%. The study also highlights the limitations of relying on dashboard cameras to detect slippery driving conditions, as it may not be accurate enough to distinguish between, for example, wet and icy road conditions. As winter maintenance often prevents slippery roads, the vehicles often measured wet or moist roads, despite RST < 0°C. Our calculations indicate that the vehicle should be able to detect 93% of slippery occasions but the rate of false warnings will be as high as 73%, if using a dashboard camera along with the AT sensor. There are clear benefits of using a RWM to improve road safety and reduce the risk of accidents due to slippery conditions, allowing AVs to safely extend their time within their ODD. The findings of this study provide valuable insights for the development of AVs and their response to slippery road conditions.</div>

A Systematic Review of Instructional Interventions Applied to Primary School Students with Mathematics Learning Difficulties

This study was undertaken to systematically analyze instructional interventions employed to enhance the mathematical performance of elementary school students with Math learning difficulties (MLD). Over 20 years (2003-2023), 34 articles that met the inclusion criteria were included in the analysis. The articles in the study were analyzed in terms of methodological and intervention characteristics. Methodological characteristics were analyzed in two categories: participant characteristics and tests for identifying students. Intervention characteristics were analyzed in six categories: grade level, instructional intervention, implementer, implementation method, intervention duration, and mathematics learning area. It was found that the articles examined generally targeted students with MLD, and standardized achievement tests were used to identify students. The articles reviewed determined that third grade students were studied as participant students and strategy teaching was frequently used as an intervention programme. In the reviewed studies, the instructional interventions applied to students with math learning difficulties were implemented mainly by expert implementers in small-group teaching. It was observed that the number of sessions for instructional interventions ranged from 4 sessions to 96 sessions, with an average intervention duration of 16.91 hours. Finally, it was determined that the instructional interventions in the analyzed studies mainly focused on the learning domain of numbers and operations as the mathematics learning area.

The vision of self-evolving computing systems

Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing systems detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.

Qualification of the PAVIN Fog and Rain Platform and Its Digital Twin for the Evaluation of a Pedestrian Detector in Fog.

Vehicles featuring partially automated driving can now be certified within a guaranteed operational design domain. The verification in all kinds of scenarios, including fog, cannot be carried out in real conditions (risks or low occurrence). Simulation tools for adverse weather conditions (e.g., physical, numerical) must be implemented and validated. The aim of this study is, therefore, to verify what criteria need to be met to obtain sufficient data to test AI-based pedestrian detection algorithms. It presents both analyses on real and numerically simulated data. A novel method for the test environment evaluation, based on a reference detection algorithm, was set up. The following parameters are taken into account in this study: weather conditions, pedestrian variety, the distance of pedestrians to the camera, fog uncertainty, the number of frames, and artificial fog vs. numerically simulated fog. Across all examined elements, the disparity between results derived from real and simulated data is less than 10%. The results obtained provide a basis for validating and improving standards dedicated to the testing and approval of autonomous vehicles.

Real-time 360 degrees view for the operator of milliAmpere 2

In the evolving domain of autonomous marine operations, accurate perception and representation of the surrounding environment are crucial for safe and effective execution. This paper addresses this issue by developing and testing a near real-time 360-degree bird’s eye view system for the situations where the ferry, milliAmpere 2, has to be manually controlled by a local operator onboard. The goal was to aid the operator during the critical phase of docking, by displaying the surrounding area of the ferry from a bird’s eye view. The bird’s eye view was made by using inverse perspective mapping on the undistorted images from the 8 cameras onboard. The system was implemented in Python, and aimed to reach a run-time of less than 200ms. This goal was reached during the initial phase of the work. However, during live testing, only near real-time performance was achieved. Despite some shortcomings, the operators found the system to be a “useful additional assistance” during the docking process.

Research on extraction method of new type of train control system operation scenario elements in special environments

At present, the construction of high-speed railway in the western region has become one of the key points in China. However, the existing train control systems do not adapt to the environment. So a new type of train control system (TCS-NT) is proposed. However, for TCS-NT, the definition and content of its operating scenarios are not clear, and the construction of operating scenarios is mostly involves subjective analysis. It faces problems such as a lack of a comprehensive operating environment analysis method, a method for selecting scenario elements for varying test requirements, and inexplicable design choices. In this paper, the Operational Design Domain (ODD) and a key element extraction method for simulation scenarios are proposed to analyze the operational environment of the TCS-NT. Using this method, the impact of each scenario element on the sub-functions is sequentially analyzed in the tracking operation scenario of the TCS-NT. According to the influence on each sub-function, a mapping equation is constructed for the three-dimensional plane comprising element, structure, and function, and scenario elements are extracted using the discriminant matrix. In this paper, the method is used to analyze the operation scenario in the train tracking scene, and the feasibility of the method is verified.

Objectively Assessing Characteristics of Mesoscale Convective Organization in an Operational Convection-Permitting Model

Abstract The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics. Significance Statement Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.

Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice.

Infection science is a discipline of healthcare which includes clinical microbiology, public health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures. The importance of infection science has become more apparent in recent years during the SARS-CoV-2 (COVID-19) pandemic and subsequent highlighting of critical operational domains within infection science including the hospital, clinical laboratory, and public health environments to prevent, manage, and treat infectious diseases. However, as the global community transitions beyond the pandemic, the importance of infection science remains, with emerging infectious diseases, bloodstream infections, sepsis, and antimicrobial resistance becoming increasingly significant contributions to the burden of global disease. Machine learning (ML) is frequently applied in healthcare and medical domains, with growing interest in the application of ML techniques to problems in infection science. This has the potential to address several key aspects including improving patient outcomes, optimising workflows in the clinical laboratory, and supporting the management of public health. However, despite promising results, the implementation of ML into clinical practice and workflows is limited. Enabling the migration of ML models from the research to real world environment requires the development of trustworthy ML systems that support the requirements of users, stakeholders, and regulatory agencies. This paper will provide readers with a brief introduction to infection science, outline the principles of trustworthy ML systems, provide examples of the application of these principles in infection science, and propose future directions for moving towards the development of trustworthy ML systems in infection science.

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

INTERNATIONAL SECURITY AND OUTER SPACE – TODAY’S LAW CHALLENGES

Space security means safe and permanent access to space and limiting threats coming from there. This definition also includes the security aspects of man-made devices sent into space and of ground stations. Space infrastructure can be described as a network of space and ground systems connected by communication channels and allowing access to space. Today, the largest space powers have begun to consider space as an operational domain of warfare. Space more and more often appears to be a field for competition, which might become an arena of conflict. The aim of this article is to present today’s many law challenges to the security of space infrastructure, such as unintentional threats (space debris, geomagnetic and solar storms, and other random disturbances), intentional threats (ASAT anti-satellite weapons, malicious interference, and cyber-attacks), the growing problems of Earth orbit congestion, and the increasing amount of space debris from devices launched into space. The article also presents the role of international organizations (such as the UN Committee on the Peaceful Uses of Outer space) in making laws that are intended to observe and react to all changes necessary in the outer space environment and to be proactive to help outer space to be safe and secure for all mankind. The conclusion is, however, not optimistic. Space security is a sensitive issue, mainly during conflicts or wars. States are not inclined to bind themselves by international law in this matter. Thus, due to the absence of hard international law (treaties), bilateral and multilateral agreements as well as the best practices from countries that organize space flights must apply. Space monitoring systems, such as the Situational Awareness System (SSA), the code of conduct in space, the UN Long Term Sustainability, or the space Traffic Management rules are legal tools to manage the above challenges in space today.

Membership function‐dependent predictive control for interval type‐2 Takagi–Sugeno fuzzy system

AbstractIn this paper, the problem of membership function‐dependent model predictive control (MFD‐MPC) of interval type‐2 Takagi–Sugeno (IT2 T‐S) fuzzy system is investigated. An online MFD‐MPC optimization problem is formulated to minimize an infinite horizon objective function subject to hard constraints on control input and system state. By using the variable dimension expansion method and dividing the operation domain of independent variables into several subdomains, a subregion‐based state feedback model predictive controller that offers more design flexibility is designed to stabilize the open‐loop fuzzy system. Based on descriptor system theory, the variable mismatching phenomenon between MFs and the coupling between plant, controller, and Lyapunov matrix are eliminated. Then, using the piecewise linear approximation technology, MFD sufficient conditions of low conservatism on guaranteeing recursive feasibility and the stability of IT2 closed‐loop fuzzy system are deduced by tightening terminal constraint set. Finally, a numerical example is provided to further explain the advance of the proposed method.