Current System Research Articles

A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems

Based on cluster system theory and the Markov process, a performance prediction method utilizing time-dependent subsystem transfers between family systems is proposed in this paper. The family system is divided via the mean clustering method, with the key performance parameters of subsystems utilized as identification parameters. According to the transition quantity of subsystems in the family systems, the transition probability of subsystems between family systems is described via the Markov process. The transition matrix between subsystems is established by dividing multiple intervals of key performance states. The inter-family transfer matrix and the current family system label of the subsystem are updated in real time. Thus, the transition probability of any subsystem and the total number of subsystems to be transferred to the failure-state family system can be judged, and the remaining life can be further determined. Using the real-world monitoring dataset from the FAST Telescope, the effectiveness and accuracy of the method are verified. Due to the representativeness of family systems to subsystems and the powerful transfer-describing ability of Markov processes, the proposed method shows superiority in online prediction and performance evaluation compared to the fault data-based method, such as improvements in rapidity and accuracy. In addition, the proposed method can be used to evaluate overall reliability without reference samples, thus making the prediction method more practical in complex, large systems with small or even zero sample conditions.

Unveiling the unseen: next-generation cardiac magnetic resonance imaging

Unveiling the unseen: next-generation cardiac magnetic resonance imaging Cardiovascular disease (CVD) claims over 1.8 million European lives yearly, surpassing cancer, chronic respiratory disease and diabetes fatalities. Cardiac magnetic resonance imaging (CMR) is crucial for understanding and treating CVD, offering radiation-free comprehensive heart assessment. However, current CMR systems are slow, complex and require specialised expertise, limiting widespread adoption. SMHEART aims to revolutionise CMR with a one-click approach, creating a unique 3D whole-heart volume for automated cardiology diagnosis, prognosis and therapy selection. This project streamlines CMR, facilitating broader acceptance and optimising its impact on personalised patient care, including diagnosis, risk stratification, therapy selection, monitoring and image-guided interventions.

Strengthening the Downstream Supply Chain Management for Vaccines in the Philippines: Food and Drug Administration Perspective

The case study examines the framework established by the Food and Drug Administration Philippines to facilitate the Vaccine Supply Chain in the Philippines. During the pandemic, the said regulatory body must ensure its efficiency and safety. AnyLogistix, a system software employed in Post Market Surveillance for vaccines in Norway, was discovered during the research process. This software has facilitated the efficient assessment and monitoring of vaccines in Norway. Consequently, this case study explores the potential benefits of implementing anyLogistix in the Philippines to enhance drug and vaccine cold chain management. The Grounded Theory was used to assess the readiness and acceptance of local companies and organizations responsible for managing cold chain facilities and vaccines in the Philippines, to pave the way for future advancements in the field. Ultimately, the acceptability of the comparative study validates the approval of stakeholders, cold chain facilities, and government regulatory agencies in using anyLogistix as an automated system to efficiently monitor the post-market surveillance in the downstream stages of the supply chain for vaccines spearheaded by the Food and Drug Administration-Philippines. This study conducted a survey among 14 expert panelists both male and female in which 8 out of 14 panelists expressed dissatisfaction with the current system of how the government responds to pandemic outbreaks, 10 out of 14 panelists were not familiar with anyLogistix, and 11 out of 14 panelists shows interest in using this Norwegian software to improve the efficiency in responding to vaccine supply management issues, therefore, anyLogistix could be a good development that the Food and Drug Administration could utilize. Keywords: Strategic continuity, Business plan, Sustainability, Adaptability, Private Emission Testing Centers, Motor Vehicle Inspection System, Regulatory pressure, Leadership change, Policy shifts, Mixed methods research.

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.

Study on the Integration of Ideological and Political Education and Students’ Mental Health Education

In the new era, the rapid advancement of Internet technology has promoted the development of social economy and education. Although the current education conditions and education system of colleges and universities have been significantly improved, the attention to students’ mental health education is low. Nowadays, there is various uneven and unhealthy information on the Internet, which will negatively impact the shaping of correct values in students, and play an essential role in improving students’ discrimination ability. Therefore, it is very important to realize the good integration between ideological and political education and students’ mental health education in college education, which can promote the all-round development of students. Based on this, this paper mainly focuses on the integration of ideological and political education and students’ mental health education, so as to provide theoretical reference for the smooth development of relevant research in the future.

A Deep Learning Approach to Merge Rule-Based and Human-Operated Camera Control for Teleoperated Robotic Systems

Controlling a laparoscopic camera during robotic surgery represents a multifaceted challenge, demanding considerable physical and cognitive exertion from operators. While manual control presents the advantage of enabling optimal viewing angles, it is offset by its taxing nature. In contrast, current autonomous camera systems offer predictability in tool tracking but are often rigid, lacking the adaptability of human operators. This research investigates the potential of two distinct network architectures: a dense neural network (DNN) and a recurrent network (RNN), both trained using a diverse dataset comprising autonomous and human-driven camera movements. A comparative assessment of network-controlled, autonomous, and human-operated camera systems is conducted to gauge network efficacies. While the dense neural network exhibits proficiency in basic tool tracking, it grapples with inherent architectural limitations that hinder its ability to master the camera’s zoom functionality. In stark contrast, the recurrent network excels, demonstrating a capacity to sufficiently replicate the behaviors exhibited by a mixture of both autonomous and human-operated methods. In total, 96.8% of the dense network predictions had up to a one-centimeter error when compared to the test datasets, while the recurrent network achieved a 100% sub-millimeter testing error. This paper trains and evaluates neural networks on autonomous and human behavior data for camera control.

Qualification of Austenitic Stainless Steels for the Development of Load-Sensitive Material Sensors

AbstractTo detect mechanical overloads on the component directly in operation, a metastable material can be used as a load-sensitive sensor when combined with an eddy current testing system. In order to find a suitable metastable sensor material that exhibits microstructural changes at an early stage before fatigue failure, quasi-static tensile tests and cyclic rotating bending tests were carried out with the austenitic stainless steels 1.4301 (2 batches), 1.4305, 1.4541 and 1.4550. For the detection of microstructural changes, electromagnetic testing was used in-situ in the tensile test and ex-situ between the rotating bending test after a pre-defined number of cycles. The investigated materials 1.4301 batch2 and 1.4550 showed the largest signal changes and the lowest austenite stability both in the tensile test and under cyclic bending load. Due to the better mechanical properties, 1.4301 batch2 should be preferred. The order of the austenitic stainless steels tested was similar in terms of transformation behavior in both tests. Thus, the tensile test combined with in-situ electromagnetic testing appears to have potential as a suitable benchmark test for austenite stability. With regard to the cyclic bending stress, an overload of the specimens could be detected for the materials 1.4301 batch2, 1.4305, 1.4541 and for the 1.4550 on the basis of a significant amplitude change. At low bending stresses, uncritical for structural integrity, no increase in amplitude was measured. The results have shown that an early detection of overloads is possible with several materials, however, the potential for detecting overloads varies between materials and also between individual batches. In addition, it has been observed that as the bending stress increases, the gradient of the change in amplitude over the number of cycles increases as well. Thus, with a known number of cycles, it could be possible to classify the previous load spectrum based on the difference in amplitude between two measurements.

Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality.

Vat Photopolymerization 3D Printing Hydrogels and Bionic Adhesive Devices: A Minireview

AbstractVivid biological structures with reversible adhesive properties are widely found in nature. Although current biomimicking adhesion systems have attracted extensive attention, unmet challenges remain in terms of excellent adhesion preservation, precise structural fabrication, and good environmental compatibility. By combining advanced vat photopolymerization 3D printing technology with hydrogel materials with excellent environmental adaptability, bioinspired adhesion devices present satisfactory application prospects. Herein, a systematic overview of bioinspired hydrogel adhesion devices from the perspectives of material design, structural design, and fabrication is presented. First, the main types and application prospects of vat photopolymerization 3D printed hydrogels are introduced. Subsequently, the research progress on hydrogel‐based vat photopolymerized 3D printed bionic adhesion devices is detailed. Future developments, such as balancing unmet challenges and expected opportunities, in 3D printable multiscale bionic adhesive devices are presented.

Reforming the Eurozone financial system: A system-dynamics approach

The effectiveness of the current monetary policy of the European Central Bank (ECB) in maintaining (price) stability in the eurozone is assessed using a system dynamic model. The model combines a neoclassical growth model with a stock-flow representation of the eurozone financial sector at a high level of aggregation. With endogenous money creation, the multidirectional relationship between asset prices and net income, and also the dual causality of both liquidity and investment relative to interest rates as the main features of the model, the developments of the past 50 years can be understood and extrapolated to 2050.The simulation experiments indicate that the current pro-cyclic system is inherently unstable. This instability can only be overcome by monetary policy that eases and tightens the total amount of money in the system in a countercyclical manner. An obvious way to achieve this is the introduction of 100% Central Bank Digital Currency (CBDC). This calls for the ECB's mandate to become the sole "monetary authority" that creates and controls the amount of money in the system, with the sole and unambiguous objective of price stability. Money creation to achieve price stability at a targeted level of inflation with simultaneous repayment of public debt varies between €200 billion and €500 billion per year. This money can be channelled into the real economy through the EU's governance structure and spent on tax cuts and/or direct public investments, for example in physical and social infrastructure.

Simulation in New Zealand: what have you done for me lately? New Zealand Association for Simulation in Healthcare (NZASH) white paper.

Medical simulation has become an integral aspect of modern healthcare education and practice. It has evolved to become an essential aspect of teaching core concepts and skills, common and rare presentations, algorithms and protocols, communication, interpersonal and teamworking skills and testing new equipment and systems. Simulation-based learning (SBL) is useful for the novice to the senior clinician. Healthcare is a complex adaptive system built from very large numbers of mutually interacting subunits (e.g., different professions, departments, equipment). These subunits generate multiple repeated interactions that have the potential to result in rich, collective behaviour that feeds back into the organisation. There is a unique opportunity in New Zealand with the formation of Te Whatu Ora - Health New Zealand and Te Aka Whai Ora - Māori Health Authority and the reorganisation of the healthcare system. This viewpoint is a white paper for the integration of SBL into our healthcare system. We describe our concerns in the current system and list our current capabilities. The way SBL could be implemented in pre- and post-registration phases of practice are explored as well as the integration of communication and culture. Interprofessional education has been shown to improve outcomes and is best done with an interprofessional simulation curriculum. We describe ways that simulation is currently used in our system and describe other uses such as quality improvement, safety and systems engineering and integration. The aim of this viewpoint is to alert Te Whatu Ora and Te Aka Whai Ora of the existing infrastructure of the simulation community in New Zealand and encourage them to invest in its future.

Overcoming the diffraction limit by exploiting unmeasured scattering media

Scattering is not necessarily an obstacle to imaging. It can help enhance imaging performance beyond the reach of a lens system. However, current scattering-enhanced imaging systems require prior knowledge of the transmission matrix. There are also some techniques that do not require such prior knowledge to see through strongly scattering media, but the results are still limited by the optics used. Here we propose overcoming the diffraction limit through a visually opaque diffuser. By controlling the distance between the diffuser and lens system, light with higher spatial frequencies is scattered into the entrance pupil. With the deformed wavefront corrected, we experimentally achieved imaging with 3.39× enhancement of the Rayleigh limit. In addition, our method works well for objects that are 4× larger than the memory effect range and can maintain super-resolution performance for a depth of field 6.6× larger than a lens can achieve. Using our method, an obstructive scattering medium can enhance the throughput of the imaging system, even though the transmission matrix of the scattering medium has not been measured beforehand.

An Analysis on the Efficiency of Alternative Dispute Resolution in Criminal Justice System with Emphasis on Plea Bargaining

Courts have long been severely overburdened with cases. It has been established that there are gaps in our current processes, and it is necessary to provide new ADR mechanisms so that conflicts can be resolved without the involvement of the court directly. ADR in criminal cases has failed to pick up speed, even though it is steadily becoming the norm for resolving civil issues. Plea bargaining is one of the ADR techniques used in the criminal judicial system. In the USA, plea bargaining is highly popular and accounts for over 90% of case resolutions. Plea bargaining has a solid track record in the United States of America for ensuring swift justice and access for all socioeconomic classes. Despite the fact that the idea of plea bargaining was first introduced to our criminal justice system back in 2005, India has not had much success with it. The many ADR techniques used in the criminal justice system are listed in this essay, along with an evaluation of their efficacy. It also compares plea bargaining practices across several nations, including Germany, the United Kingdom, France, India, and the United States, and examines whether India's current plea-bargaining system is effective enough to address the issue of the backlog of cases in our courts. The paper also makes recommendations to the legislative body to improve the effectiveness and efficiency of plea negotiations.

Of editorial processes, AI models, and medical literature: the Magnetic Resonance Audiometry experiment.

The potential of artificial intelligence (AI) in the field of medical research is unquestionable. Nevertheless, the scientific community has raised several concerns about a possible fraudulent use of these tools that might be used to generate inaccurate or, in extreme cases, erroneous messages that could find their way into the literature. In this experiment, we asked a generative AI program to write a technical report on a non-existing Magnetic Resonance Imaging technique called Magnetic Resonance Audiometry, receiving in return a full seemingly technically sound report, substantiated by equations and references. We have submitted this report to an international peer-reviewed indexed journal, passing the first round of review with only minor changes requested. With this experiment, we showed that the current peer-review system, already burdened by the overwhelming increase in number of publications, might be not ready to also handle the explosion of these techniques, showing the urgent need for the entire community to address both the issue of generative AI in scientific literature and probably a more profound discussion on the entire peer-review process. CLINICAL RELEVANCE STATEMENT: Generative AI models are shown to be able to create a full manuscript without any human intervention that can survive peer-review. Given the explosion of these techniques, a profound discussion on the entire peer-review process by the scientific community is mandatory. KEY POINTS: • The scientific community has raised several concerns about a possible fraudulent use of AI in scientific literature. • We asked a generative AI program to write a technical report on a non-existing technique, receiving in return a full technically sound report, substantiated by equations and references, that passed peer-review. • This experiment showed that the current peer-review system might be not ready to handle the explosion of generative AI techniques, advising for a profound discussion on the entire peer-review process.

Drift Adaptive Online DDoS Attack Detection Framework for IoT System

Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.

Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation

Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to elaborate implementations of systems integrated into everyday product settings; it may target a wide spectrum of different factors being evaluated. In this special issue, we explore recommender systems evaluation—theory and practice—while considering a diverse set of perspectives. These include recommender systems purposes, stakeholders, methodological approaches, and consequences. The collection of articles in this special issue offers insightful analyses of current recommender system evaluation practices, acknowledging their limitations, and setting out future research directions. As recommender systems evolve, the need for adequate evaluation methods and approaches increases. This special issue sheds light on areas undergoing development or requiring added attention from the research and practitioner communities in recommender systems. The compilation serves as a call to the recommender systems research community, motivating continued research and exploration of evaluation metrics, methods, and strategies.

Perceived social mobility and system justification predict greater well‐being, but less prosocial behaviour

AbstractIn the present research, we propose that perceptions of social mobility (PSM) are beneficial for oneself but costly to others. Supporting this idea, people who were led to think that social mobility in society is probable (vs. improbable) (Study 1a/b, N = 754; Study 3a/b, N = 938) or held this belief at a dispositional level (Study 2a/b, N = 877) showed greater endorsement of system justifying beliefs, which was related to greater happiness and life satisfaction. However, the more people perceived social mobility and justified the system, the less willing they were to help others in need, and this was especially true for those who thought the current economic system was fair and legitimate. Thus, while greater perceived social mobility is related to increased personal well‐being through justification of the sociopolitical system, it predicts less desire to help others due to increased support of the economic status quo.

Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction

Image guidance is an important adjunct for endoscopic sinus and skull base surgery. However, current systems require bulky external tracking equipment, and their use can interrupt efficient surgical workflow. To evaluate a trackerless surgical navigation system using 3-dimensional (3D) endoscopy and simultaneous localization and mapping (SLAM) algorithms in the anterior skull base. This interventional deceased donor cohort study and retrospective clinical case study was conducted at a tertiary academic medical center with human deceased donor specimens and a patient with anterior skull base pathology. Participants underwent endoscopic endonasal transsphenoidal dissection and surface model reconstruction from stereoscopic video with registration to volumetric models segmented from computed tomography (CT) and magnetic resonance imaging. To assess the fidelity of surface model reconstruction and accuracy of surgical navigation and surface-CT model coregistration, 3 metrics were calculated: reconstruction error, registration error, and localization error. In deceased donor models (n = 9), high-fidelity surface models of the posterior wall of the sphenoid sinus were reconstructed from stereoscopic video and coregistered to corresponding volumetric CT models. The mean (SD; range) reconstruction, registration, and localization errors were 0.60 (0.24; 0.36-0.93), 1.11 (0.49; 0.71-1.56) and 1.01 (0.17; 0.78-1.25) mm, respectively. In a clinical case study of a patient who underwent a 3D endoscopic endonasal transsphenoidal resection of a tubercular meningioma, a high-fidelity surface model of the posterior wall of the sphenoid was reconstructed from intraoperative stereoscopic video and coregistered to a volumetric preoperative fused CT magnetic resonance imaging model with a root-mean-square error of 1.38 mm. The results of this study suggest that SLAM algorithm-based endoscopic endonasal surgery navigation is a novel, accurate, and trackerless approach to surgical navigation that uses 3D endoscopy and SLAM-based algorithms in lieu of conventional optical or electromagnetic tracking. While multiple challenges remain before clinical readiness, a SLAM algorithm-based endoscopic endonasal surgery navigation system has the potential to improve surgical efficiency, economy of motion, and safety.

Hydrological drought forecasts using precipitation data depend on catchment properties and human activities

Hydrological Drought Early Warning Systems play a crucial role in effective drought planning and management, as the impacts of drought are more closely associated with hydrological droughts than meteorological ones. However, current systems primarily focus on meteorological drought forecasts due to the limited access to hydrological data. Here we assess the feasibility of forecasting drought in streamflow and groundwater by solely using precipitation data. The results demonstrate that meteorological drought forecasts derived from the Standardized Precipitation Index with 6-month accumulation periods and various lag times hold the potential to predict streamflow and groundwater droughts. This study also highlights the importance of catchment properties in hydrological drought predictions. Our findings present an opportunity for developing hydrological drought early warning system globally to reach the goal of the Sendai framework for disaster risk reduction by 2030 and support the initiative of early warnings for all.

Supporting the professional sports coach in the workplace: presenting the pedagogue as the missing scientist

ABSTRACT This is a positional paper in which we consider (re)imagining the role of the pedagogue as one who is specifically employed in a multi-disciplinary team of experts that can advise the professional sports coach. Our rationale is underpinned by multiple factors including the low impact of coach education, issues with knowledge translation, and current prevailing discourses in elite sport. Consequently, we employ ethnodrama as a “way in” to explore the precarious existence of elite coaches, cohering specifically around the profligate “hiring and firing” cultures found in professional football. This enables the inquiry to highlight how the pedagogue – the missing scientist – can utilise practical solutions on the ground responding to real-world problems. Going further, we consider how this role could also become a pivotal figure of resistance to the current value system inherent within elite sport reported to jeopardise athletes’ health and wellbeing. Thus, we respond to numerous challenges replete in elite sport organisations that are attracting increasing attention.