Virtual Monitor Research Articles

Testing ADAS/ADS – from critical scenarios to automated testing oracles

AbstractAutomated and autonomous driving systems are increasingly integrated into modern vehicles to support the vision of safe, efficient, and comfortable transportation. Given the complexity of these systems, thorough testing and close monitoring of their behavior is inevitable to prevent hazardous situations and unexpected behaviors. This paper presents different approaches for testing ADAS/ADS, focusing on generating critical scenarios for virtual validation and monitoring approaches applied during operation. Specifically, we provide a comprehensive overview of our previous work on combinatorial and search-based testing methodologies, highlighting their application in generating robust test suites. Additionally, we summarize our work on intelligent monitoring approaches to detect operational issues. Our findings emphasize the necessity of advanced testing solutions and continuous monitoring to identify and mitigate potential failures, demonstrating their applicability in enhancing the safety and trustworthiness of ADAS/AD.

Advanced Practice Nurse-Led Research: Challenges and Approaches to Digital Health Programs' Evaluation Using Big Data.

Advanced practice nurse leaders are in key positions within health systems to provide time and resources for implementation and evaluation of digital health services. As virtual monitoring programs become more embedded within nursing, nurse leaders and educators need to ensure that nurses are prepared to work within interprofessional teams to administer and evaluate them. This article discusses challenges and implementation strategy considerations for data curation and analysis using large datasets from the Medicaid population for research.

Security Evaluation Framework for Virtualised Environments

With the expansion of cloud computing, virtual environments remain susceptible to sophisticated security threats, such as hypervisor vulnerabilities, VM escapes, inter-VM attacks, DoS attacks, and malware injections. Hence, ensuring that virtualised environments are secure, has become increasingly crucial. These threats are particularly affecting as many enterprises which have shifted to online services and remote work. This paper examines the existing frameworks which have been designed to address the existing challenges, their gaps and strengths; and proposes an enhanced security framework that can help to mitigate these threats. The framework was developed following an iterative process encompassing several stages. It has the hypervisor layer, virtual machine layer, network layer, management layer, and monitoring and response layer components. The framework aims to enhance detection capabilities, reduce response times, minimise system performance impact, and lower false positive rates while optimising resource utilisation by providing a practical and effective approach to securing virtualised infrastructures, thereby ensuring the resilience and reliability of cloud-based services.

Enhancing palliative care for advanced cancer patients: evaluating implementation and impact of a virtual nurse-led symptom monitoring and telehealth initiative

BackgroundAs a result of the COVID-19 pandemic, we implemented a novel nurse-led symptom monitoring and virtual telehealth program for patients with advanced cancer on palliative care.AimsTo evaluate the reach, effectiveness, adoption, implementation, maintenance and acceptability of our program.MethodsThis was a prospective study carried out in a cohort of patients with advanced cancer over the period of 3rd June 2020 to 22nd October 2021. Demographic characteristics, cancer diagnosis, and functional status of patients were collected upon recruitment. Patients were asked to complete a patient-reported outcome measure (the Integrated Palliative Care Outcome Scale, IPOS) prior to the first palliative care consult and subsequently every week for the duration of their participation in the program (12 weeks). The IPOS measures the severity of physical symptoms, emotional concerns, information, and financial needs. Participants’ utilization of healthcare services by participants 3 months before, during and 3 months after the telemedicine program was reviewed. At the end of the program, a client satisfaction questionnaire (CSQ-4) to survey participants’ experience with the telehealth program and their willingness to pay for this program was administered.ResultsReach: The recruitment to adoption ratio of the program was 0.71. Acceptability:: Participants expressed satisfactory experience. Effectiveness: We noted that the severity of patients’ symptoms and number of emergency department visits decreased over time with nurse support. Adoption: we received referrals from 23 oncologists, yielding an adoption rate of 70%. Implementation: Of the 99 patients recruited for the program, 88.9% of them managed to complete their initial video consults as planned. 16% of them failed to complete the program due to factors such as patient demising. Maintenance: The declining rate of IPOS completion throughout the study period (98.9% at week 1 to 60.8% at week 12) demonstrated the difficulties in sustaining regular administration of self-reported patient outcome measures.ConclusionThe telemedicine program was effective and acceptable. We noted challenges in sustaining the administration of patient reported outcome measures over time. Further studies on how we can improve the sustainability of symptom monitoring in a telehealth program for patients with advanced illnesses, under palliative care, should be conducted.

Concept of Integrating a Hybrid Thermal Error Compensation Into an Existing Machine Tool Control Architecture

Thermal error compensation via a numeric control (NC) system is a proven option for upgrading the precision of machine tools. The main advantage is the generally cost-effective application, as no changes to the machine design are necessary. Since modern machine tools are equipped with standard numeric controls along with additional functions and integrated temperature sensors in the machine, compensation methods such as a characteristic diagram (CD) based compensation can be implemented. To increase the applicability and reliability of this CD regression method, a hybrid model approach with a virtual thermo-elastic finite element (FE) machine model and a real-time computable structural model of a machine tool was developed. The structural model uses model order reduction to calculate the current load case in real-time using continuously recorded machine data (motor current, axis position, temperatures). It acts as a virtual monitoring application to check, whether the current machine condition still matches the current CD based prediction. If the current load case is not suitable to the active CDs or any other stored CDs, the generation of new CDs is automatically triggered. In this article, the integration of the hybrid compensation method using an FE model and a structural model of a machine tool is methodically demonstrated. The main focus is on the integration of different software and hardware architectures and their interaction.

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

Multi-mission virtual monitoring station for streamflow monitoring and hydrodynamic model calibration

A comprehensive understanding of the streamflow dynamic along the river system is one of the significant components for preserving biodiversity and ensuring sustainable ecological processes. Understanding this requirement, streamflow estimation using remote sensing (RS) has evolved as an alternate approach in the hydrologic literature during the last decade due to its extensive spatiotemporal coverage. However, the existing RS-based approaches have limited applications for deriving the continuous time series in tropical river reaches due to narrow water widths during the low flow and frequent cloud covers during the high flow periods. Therefore, this study proposed frameworks to establish a virtual monitoring station (VMS) where multi-mission satellites are being used to derive continuous streamflow time series. From the multi-mission satellites, the optical RS images are used to develop a Copula-based fusion (CFUS) model by integrating the Frank copula with the 30m × 1-day resolution synthetic Landsat images, derived from the fusion of 250m × 1-day resolution MODIS images and 30m × 16-days resolution Landsat images; whereas the water levels are retrieved from the altimeters to derive discharge using the rating curve. Additionally, the potential utility of the established VMS for hydrodynamic model (MIKE11-NAM-HD) calibration under data-scarce conditions is also demonstrated. Finally, a coupled RS-hydrodynamic (RS-HD) framework is also proposed to establish VMS both at semi-gauged and ungauged sections of the rivers. The efficacy of the aforementioned framework was tested along the lower Brahmani river reach. The results reveal that the advocated framework could perform satisfactorily with the Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) ≥0.8. This approach has the potential to be upscaled to other river reaches as one of the next-generation hydrometry for daily streamflow monitoring using high-frequent observation of multi-mission satellites.

Understanding the role of video direct observed therapy for patients on an oral short-course regimen for multi-drug resistant tuberculosis: findings from a qualitative study in Eswatini

BackgroundImproving treatment success rates among multi drug-resistant tuberculosis (MDR-TB) patients is critical to reducing its incidence and mortality, but adherence poses an important challenge. Video-based direct observed therapy (vDOT) may provide adherence benefits, while addressing the time and cost burden associated with community treatment supporter (CTS)-DOT. This study explored experiences of patients, family members and healthcare workers with different DOT modalities for adherence support in Eswatini.MethodsBetween April 2021 and May 2022, thirteen men and five women with MDR-TB, ten healthcare workers, and nine caregivers were purposively sampled to include a range of characteristics and experiences with DOT modalities. Data were generated through individual in-depth interviews and a smartphone messaging application (WhatsApp). Data coding was undertaken iteratively, and thematic analysis undertaken, supported by Nvivo.ResultsFour themes emerged that reflected participants’ experiences with different DOT modalities, including stigma, efficiency, perceived risks of TB acquisition, and patient autonomy. vDOT was appreciated by patients for providing them with privacy and shielding them from stigmatisation associated with being seen in TB clinics or with community treatment supporters. vDOT was also seen as more efficient than CTS-DOT. Health workers acknowledged that it saved time, allowing them to attend to more patients, while many patients found vDOT more convenient and less expensive by removing the need to travel for in-person consultations. Health workers also appreciated vDOT because it reduced risks of TB acquisition by minimising exposure through virtual patient monitoring. Although many patients appreciated greater autonomy in managing their illness through vDOT, others preferred human contact or struggled with making video recordings. Most family members appreciated vDOT, although some resented feeling removed from the process of supporting loved ones.ConclusionsvDOT was generally appreciated by MDR-TB patients, their family members and health workers as it addressed barriers to adherence which could contribute to improved treatment completion rates and reduced workplace exposure. However, patients should be offered an alternative to vDOT such as CTS-DOT if this modality does not suit their circumstances or preferences.

A Virtual Solar Wind Monitor at Mars With Uncertainty Quantification Using Gaussian Processes

AbstractSingle spacecraft missions do not measure the pristine solar wind continuously because of the spacecrafts' orbital trajectory. The infrequent spatiotemporal cadence of measurement fundamentally limits conclusions about solar wind‐magnetosphere coupling throughout the solar system. At Mars, such single spacecraft missions result in limitations for assessing the solar wind's role in causing lower altitude observations, such as auroral dynamics or atmospheric loss. In this work, we detail the development of a virtual solar wind monitor from the Mars Atmosphere and Volatile Evolution (MAVEN) mission; a single spacecraft. This virtual solar wind monitor provides a continuous estimate of the solar wind upstream from Mars with uncertainties. We specifically employ Gaussian process regression to estimate the upstream solar wind and uncertainty estimations that scale with the data sparsity of our real observations. This proxy enables continuous solar wind estimation at Mars with representative uncertainties for the majority of the time since late 2014. We conclude by discussing suggested uses of this virtual solar wind monitor for statistical studies of the Mars space environment and heliosphere.

Navigating Social Work Field Education during the COVID-19 Pandemic: Challenges, Benefits and Opportunities

In March 2020, the University of Guyana suspended face-to-face activities, requiring academic programmes and social work field education to reconfigure to virtual modalities. This paper revisits the impact of COVID-19 on social work education to examine the challenges, benefits, and opportunities to lecturers and students. Using a qualitative approach, a document analysis was conducted on lecturers’ journal entries and field notes, and 56 reflective reports submitted by students. The findings revealed self-reports of anxiety, loss of support of practice teachers, disappointment, and burnout. In spite of the challenges experienced, the analysis revealed that the peer-substitution practice dyads allowed students to develop their interpersonal and counselling skills. Additional findings suggest that there were opportunities for lecturers and the Social Work Unit to pilot the use of information communication technologies, create a framework for virtual monitoring of students, and demonstrate their commitment to preserving the signature pedagogy of social work education. We conclude that the COVID-19 pandemic has served as an impetus for the national University to continue exploring and implementing new approaches to advance the growth of social work programmes.

Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm

Virtual monitoring method for computer network security in earth observation communication using multi ant colony random walk classification algorithm

48‐1: A Data‐Driven Intelligent Stress Monitoring for a Robust Manufacturing of a Phone Display with the Extremely Narrow Bottom Bezel

A data‐driven intelligent stress monitoring methodology is proposed for a robust manufacturing of a phone display with the extremely narrow bottom bezel. For the narrow bottom bezel, a bottom of a display panel should be bent 180 degrees with asymmetric bended protrusion by tight attachment. The mechanical stress of the bended panel increases rapidly depending on the amount attached after bending 180 degrees. For stable operation of assembly process with high risk process method, the virtual stress monitoring are applied for total product samples during the process. The intelligent stress sensor produces the virtual stress/strain data using a surrogated model, which is learned with the dataset of measured data and simulated data of the tightly bending attachment using it as input. The virtual stress/strain data which matches 99.4% of the simulation value with regression error of less than 1% can be obtained in real time from all products during assembly process with bending of the display panel. The crack risks of the bending 180 degrees are monitored in real time for anomaly detection using the virtual stress data produced by the proposed virtual sensor. With these approaches, we believe that the manufacturing processes having high risks could be stably controlled for a real infinite display creating an incredibly immersive viewing experience.

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber-physical optimization system.

Exploring the Efficacy of Machine Learning Tools for Analyzing Student Performance in Computer Science Applications

This research study examines how ML techniques can assess computer science student performance. The study compares current and past performance metrics, identifies salient performance assessment features, and outlines strategies for sustaining or enhancing student achievement in the context of the COVID-19 pandemic, which has shifted the paradigm toward online learning. Context: With the rise of digital learning platforms and the changing educational environment, ML approaches are being used to get useful insights from massive student data sets. The COVID-19 pandemic has highlighted the need for improved virtual student learning monitoring and support methods. Objective: The purpose of this study is to determine if ML methods can accurately assess student computer science proficiency. Compare students' current performance measures to their prior records to identify COVID-19-related changes. To choose relevant characteristics for performance evaluation using ML algorithms and statistics. Methods: The research uses a mixed-methods approach, combining quantitative academic record analysis with qualitative student survey and interview data. Results: show substantial relationships between performance metrics and external influences, including remote learning. ML models identify academic performance factors, enabling targeted interventions and instructional improvements. Conclusion: Integrating ML technologies can improve computer science instruction and student achievement. Data-driven insights help instructors change teaching tactics, identify at-risk pupils, and create a dynamic learning environment that promotes academic success. ML techniques may help educational institutions negotiate distant learning and achieve inclusive, equitable, and successful results.

Improvement of performance of in-situ virtual monitoring system of the occurrence probability for high concentrations of naturally occurring radioactive materials in groundwater through the solution of the data imbalance problem

This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; 238U and 222Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for 238U and 222Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater.

Evaluating the Quality of Virtual Urgent Care: Barriers, Motivations, and Implementation of Quality Measures.

Experts estimate virtual urgent care programs could replace approximately 20% of current emergency department visits. In the absence of widespread quality guidance to programs or quality reporting from these programs, little is known about the state of virtual urgent care quality monitoring initiatives. We sought to characterize ongoing quality monitoring initiatives among virtual urgent care programs. Semi-structured interviews of virtual health and health system leaders were conducted using a pilot-tested interview guide to assess quality metrics captured related to care effectiveness and equity as well as programs' motivations for and barriers to quality measurement. We classified quality metrics according to the National Quality Forum Telehealth Measurement Framework. We developed a codebook from interview transcripts for qualitative analysis to classify motivations for and barriers to quality measurement. We contacted 13 individuals, and ultimately interviewed eight (response rate, 61.5%), representing eight unique virtual urgent care programs at primarily academic (6/8) and urban institutions (5/8). Most programs used quality metrics related to clinical and operational effectiveness (7/8). Only one program reported measuring a metric related to equity. Limited resources were most commonly cited by participants (6/8) as a barrier to quality monitoring. We identified variation in quality measurement use and content by virtual urgent care programs. With the rapid growth in this approach to care delivery, more work is needed to identify optimal quality metrics. A standardized approach to quality measurement will be key to identifying variation in care and help focus quality improvement by virtual urgent care programs.

Enhancing tuberculosis treatment adherence and motivation through gamified real-time mobile app utilization: a single-arm intervention study

BackgroundFinding innovative methods to enhance Tuberculosis treatment adherence in Malaysia is imperative, given the rising trend of non-adhere TB patients. Direct Observed Therapy (DOTS) has been used to ensure Tuberculosis (TB) drug compliance worldwide. However, due to its inconvenience, digitalizing this system into a virtual monitoring system via a mobile app can help deliver a more efficient tuberculosis management system. A gamified video-observed therapy is developed that connects three users the patient, supervisor, and administrator, allowing drug monitoring and patient loss to follow up with the patient tracking system. Thus, the objective of this study is to determine the impact of Gamified Real-time Video Observed Therapy (GRVOTS) mobile apps on patient medication adherence rates and motivation.Methods71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.ResultsThis study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p < 0.001). The participants’ MyMAAT and IMI scores significantly increased over 3-time intervals with the IMI Interest domain showing the highest mean difference 19.76 (95% CI: 16.37, 21.152: p < 0.001).ConclusionsBy utilizing GRVOTS, a mobile application based on gamification and real-time features, we can enhance motivation and medication adherence among TB patients, while also addressing the limitations of physical DOTS.Trial registrationIRCT20230308057657N1, Registered on (15/03/23).

Machine learning enabled robot-assisted virtual health monitoring system design and development

Machine learning enabled robot-assisted virtual health monitoring system design and development

Telepalliative Care in Home-Based Nursing Care for Older Adults With Metastatic Cancer Post COVID-19: An Ethnoscientific Study.

The post-COVID-19 pandemic caused the whole world to make several changes to the nursing healthcare system. This sudden shift raised questions about telepalliative care in home-based nursing care in the context of healthcare utilization, including meeting the needs of older adults with metastatic cancer. The evidence suggests that telepalliative care in home-based nursing care is acceptable to most advanced practice nurses, but the extent of their use for metastatic cancer patients has not been defined. To explore the use of telepalliative care in home-based nursing care for older adults with metastatic cancer in central Thailand following the post-COVID-19 pandemic. A qualitative approach with an ethnoscientific design was used to collect data from a purposive sample of 15 advanced practice nurses from May to September 2023. The interview transcripts were analyzed using componential analysis (core coding, categorizing, theme, and emerging theory). The componential analysis revealed two themes of telepalliative care in home-based nursing care for older adults with metastatic cancer in the post COVID-19 pandemic. The first theme is telepalliative care delivery, with subthemes of delivering practice, nurse-mediated feedback and supportive care, remote monitoring of real-time emergencies, and transferring medical data. The second theme is advanced practice nurses' (APN) role in telepalliative nursing care, including the subthemes of virtual monitoring, life-threatening cancer, side effects, caregiving capacity, continuity of care, and long-term care services. The study found that caregiving capacity, continuity of care, and long-term care services were formed of telepalliative care in home-based nursing care for cancer patients following the COVID-19 pandemic. Telepalliative care in home-based nursing care is effective in delivering services to older adults with metastatic cancer during the post-COVID-19 pandemic. The findings suggested that remote monitoring of real-time emergencies, life-threatening cancer, and long-term care services are part of telepalliative care in home-based nursing care. Adapting the telepalliative competency standards of advanced practice nurses is needed to ensure high-quality healthcare access for older adults with metastatic cancer during the post-COVID-19 pandemic.