Virtual System Research Articles

Is there a Solution to Lengthy Hospital Stays for Nursing Home Residents? Our Nursing Home Liaison Team Are Virtually There

Abstract Background Research indicates that Nursing Home residents frequently undergo transfers to acute care, leading to adverse events and functional decline (Brucksch et al., 2018). Local data from highlights the significance of this issue, with 447 admissions averaging 11.1 days in 2022 (HIPE, 2023). Hospital at Home is recognised as a cost-effective alternative for older patients (Singh et al., 2022). The proposed solution involves implementing Virtual Wards, remote telemetry, and a Nursing Home Liaison team led by a Consultant Geriatrician. This initiative targets Early Supported Discharge to reduce the hospital length of stay for Nursing Home patients. The study aimed to investigate the feasibility of using the Virtual Ward platform in this service. Methods The study aimed to investigate the feasibility of using the Virtual Ward platform, primarily utilising the Telehealth Usability Questionnaire (TUQ) (Parmanto et al., 2016). A Simulation Lab was also used to test the digital solution before its clinical implementation. Additionally, debriefing sessions were conducted to assess clinicians' perceptions of the technology and identify any potential issues. Results The TUQ scores for clinicians using a Virtual Ward for Nursing Home patients show a notably positive outcome. With an average score of 120.5 out of 147, the system received significant approval. The range of scores, from 111 to 139, demonstrates a consistent positive perception among respondents. After simulation-based training in the Simulation Lab, participants reflected on their experiences with Virtual Ward technology. They found it useful, easy to navigate and committed to future use. However, they also identified areas, such as information retrieval, needing refinement. Increased training was suggested to address these concerns. Conclusion The Virtual Ward system demonstrates significant approval. In light of these promising results, we are proceeding with the plan to launch the Virtual Wards in May 2024 to complement the existing team.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

Towards Emotionally Intelligent Virtual Environments: Classifying Emotions through a Biosignal-Based Approach

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.

FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning.

Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) algorithms to analyze and predict PICU LOS based on historical patient data from the VPS database. The study included data from over 100 North American PICUs spanning the years 2015-2020. After excluding entries with missing variables and those indicating recovery from cardiac surgery, the dataset comprised 123,354 patient encounters. Various ML models, including Support Vector Machine, Stochastic Gradient Descent Classifier, K-Nearest Neighbors, Decision Tree, Gradient Boosting, CatBoost, and Recurrent Neural Networks (RNNs), were evaluated for their accuracy in predicting PICU LOS at thresholds of 24 h, 36 h, 48 h, 72 h, 5 days, and 7 days. Results: Gradient Boosting, CatBoost, and RNN models demonstrated the highest accuracy, particularly at the 36 h and 48 h thresholds, with accuracy rates between 70 and 73%. These results far outperform traditional statistical and existing prediction methods that report accuracy of only around 50%, which is effectively unusable in the practical setting. These models also exhibited balanced performance between sensitivity (up to 74%) and specificity (up to 82%) at these thresholds. Conclusions: ML models, particularly Gradient Boosting, CatBoost, and RNNs, show moderate effectiveness in predicting PICU LOS with accuracy slightly over 70%, outperforming previously reported human predictions. This suggests potential utility in enhancing resource and staffing management in PICUs. However, further improvements through training on specialized databases can potentially achieve better accuracy and clinical applicability.

A randomised controlled implementation study integrating patient self-screening with a remote central monitoring system to screen community-dwellers aged 75 years and older for atrial fibrillation.

Diagnosis of atrial fibrillation (AF) provides opportunities to reduce stroke risk. This study aimed to compare AF diagnosis rates, participant satisfaction and feasibility of an electrocardiogram (ECG) self-screening virtual care system with usual care. This randomised controlled implementation study involving community-dwelling people aged ≥75 years was conducted from May 2021 to June 2023. Participants were given a handheld single-lead ECG device and trained to self-record ECGs once daily on weekdays for 12 months. The control group received usual care with their general practitioners in the first 6 months and participated in the subsequent 6 months. AF diagnosis and participant satisfaction were assessed at 6 months. 200 participants (mean age 79.0±3.4 years; 54.0% female; 72.5% urban). AF was diagnosed in 10/97 (10.3%) intervention participants and 2/100 (2.0%) in the control group (Odds Ratio 5.6, 95% CI 1.4-37.3, p=0.03). In the intervention, 80% of AF cases were diagnosed within 3 months. 91/93 (97.9%) intervention participants and 55/93 (59.1%) control-waitlisted participants (p<0.001) were satisfied with AF screening. Of the expected 20 days per month, the overall monthly median number of days participants self-recorded ECGs was 20 (interquartile range 17-22). Participants were confident using the device (93%), reported it was easy to use (98%) and found screening efficient (96%). Patient-led AF self-screening using single-lead ECG devices with a remote central monitoring system was feasible, acceptable, and effective in diagnosing AF among older people. This screening model could be adapted for implementation, interfacing with integrated care models within existing health systems.

Design and Implementation of a Virtual Experimental Teaching System for Deep Energy Exploitation Based on Digital Twin Technology

The exploration and development of deep oil and gas resources are becoming the primary focus in the fossil energy sector, thereby increasing the demand for highly skilled engineers. Colleges and universities play a crucial role in cultivating talent in petroleum engineering. However, the current traditional teaching systems, particularly in experimental practices, face significant challenges, such as low efficiency, limited environments, and a disconnect between theoretical knowledge and practical application. To address these issues and enhance learners’ practical abilities and comprehension, we introduced digital twin technology into the experimental teaching of deep energy exploitation. This paper analyzes innovative pedagogical approaches, with a special emphasis on the real-time visualization of hydraulic fracturing. Supported by the National Key Laboratory of Chengdu University of Technology, our research team developed multiple digital twin platforms for both indoor and onsite hydraulic fracturing. These platforms utilize advanced algorithms and models, enabling real-time data acquisition and visualization analysis. Pilot teaching results demonstrate that the virtual experimental system based on digital twin technology encourages active learner engagement, improves their understanding of digitalization in engineering, and enhances their professional skills in deep oil and gas exploration. The digital twin-based visualization system is a valuable tool for experimental teaching in deep energy exploitation, and its application could serve as a model for other engineering disciplines.

Measurement and optimization method for aero-engine rotors based on binocular multi-line laser sensing and virtual assembly

During the assembly of aero-engine rotors, eccentricity and tilt errors along the radial and axial direction result in uneven loading along the mating surface, which significantly impacts the operational lifespan of the rotor. This paper proposes a three-dimensional contour measurement system based on binocular multi-line laser sensing and conducts an aircraft engine rotor stacking virtual assembly technology system, to response the practical requirements of aircraft engine rotor unit production assembly engineering. We combine multi-line structured light active projection device with the binocular stereo vision model to construct the contour measurement system, which enhances the surface features of the projected object by adhering to the common constraint of structured line and stereo vision. Furthermore, the error propagation model for multi-stage rotor assembly to investigate the mechanism of error impact is established aiming to facilitate quantitative research on virtual assembly mechanisms. By manipulating the rotor phases and evaluating the coaxiality under various conditions, effective prediction of phase variation can be achieved, the multi-objective benchmark virtual assembly method based on the error propagation model of multi-stage rotor coaxiality is presented. The experimental results indicate that the proposed measurement system achieves the maximum disparity between the spatial data points procured by the proposed measurement system and the predetermined value of 2.1 μm, and the maximum disparity between the target plane’s average movement distance and the preset value of 3.7 μm. The precision provided by this system effectively meets the objective of assembly phase optimization for multi-stage rotors. In the empirical experiment conducted on the three-stage rotors, both systems yielded identical optimized phases: 0° for the first-stage rotor, 270° for the second-stage rotor, and 120° for the third-stage rotor, thereby validating the efficacy of the proposed measurement and optimization method for aero-engine multi-stage rotors with virtual assembly.

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Adoption of virtual library systems and services in university libraries: an empirical investigation from the Punjab, Pakistan

Purpose This study aims to identify the status of virtual library system development in university libraries. It also attempted to examine the availability of virtual library services and reveal the challenges being faced by university librarians of Punjab province, Pakistan in providing virtual library services. Design/methodology/approach The study used a quantitative research method by using a questionnaire to address the study’s objectives. In total, 62 university libraries in Punjab province, Pakistan were the population of the study. A purposive sampling technique was applied to gather the required data from the respondents. Findings Results showed that most of the study’s respondents are well aware of the potential benefits of virtual library systems in universities for the delivery of innovative smart services to library patrons. The libraries delivered different virtual library services to meet the diverse needs of the library patrons. The respondents faced the challenges of funding, expertise, technical support, leadership, strategic planning and training to adopt virtual library systems and services in university libraries. Originality/value The study has provided a framework based on empirical findings of the study to efficiently adopt virtual library systems and services in university libraries for the delivery of smart services to end users. It has provided pertinent theoretical and practical implications.

An Optimized Power-Angle and Excitation Dual Loop Virtual Power System Stabilizer for Enhanced MMC-VSG Control and Low-Frequency Oscillation Suppression

Modular Multilevel Converter Virtual Synchronous Generator (MMC-VSG) technology is gaining widespread attention for its ability to enhance the inertia and frequency stability of the power grid integrated with converter-interfaced renewable energy sources. However, the excitation voltage regulation in the MMC-VSG can generate equivalent negative damping torque and cause low-frequency oscillation problems similar to those in synchronous machines. This article aims to improve the system’s damping torque and minimize low-frequency oscillations by introducing a Virtual Power System Stabilizer (VPSS) into the power control loop. Building on the study of dynamic interactions between various control links of the MMC, this research establishes a reduced-order model (ROM) and a Phillips–Heffron state equation for the MMC-VSG single machine infinite bus system, using a hybrid modeling approach and a zero-pole truncation method. It also analyzes the mechanism of low-frequency oscillations in the MMC-VSG system through the damping torque method. The analysis reveals that the negative damping torque produced during the excitation voltage regulation process causes changes in the virtual power angle, which in turn increases the risk of low-frequency oscillation in the MMC-VSG. To address this issue, the article proposes an optimized control method for the MMC-VSG dual power loop architecture (power-angle/excitation) VPSS. This strategy compensates for the inadequate damping torque of a single loop VPSS and effectively suppresses low-frequency oscillations in the system.

Pixel-wise Phase Map Fusion Technique for High Dynamic Range 3-D Shape Measurement

Abstract Digital fringe projection technologies have been extensively applied in 3-D shape measurements. However, it is still limited to achieve accurate 3-D reconstruction of high reflective surfaces. To circumvent this problem, we propose a complete framework to realize pixel-by-pixel phase post-compensation of overexposed points, including the construction of hardware system and the guidelines of algorithm design. First, a virtual binocular vision system is established. Second, based on the fundamentals of projective and spatial transformation, three robust algorithms with low computational complexity are devised to generate a fused phase map that overcomes the information loss due to overexposure. Furthermore, experiments are conducted to evaluate the performance of the proposed method. Qualitative experiment results show that the presented technique achieve a similar surface coverage compared with the other two mainstream methods, but is superior to them in terms of measurement efficiency (13 images vs. 144 images, 13 images vs. 32 images). Quantitative experimentation further verifies the feasibility of our method in precise 3-D measurement, with sphere diameter errors of 0.0367mm and 0.0361mm, spherical centroid distance error of 0.0269mm, and plane distance error of 0.0186mm.

Retraction Note: Application of optical sensor motion capture based on wearable devices in the virtual system of college aerobics teaching

Retraction Note: Application of optical sensor motion capture based on wearable devices in the virtual system of college aerobics teaching

Semantically Modified Proverb in the System of German-language Virtual Communication

The object of study in the article is the German proverb Ohne Fleiß kein Preis in the German-language virtual communication system, namely Germany, Austria, Switzerland. The subject of research is the types of semantic modifications of the above sentiment. The purpose of the study is to highlight the features of the functioning of the semantically modified form of the proverb on the information and advertising portals of Germany, Austria, Switzerland. The theoretical part of the article describes the specifics of the linguistic aspect of virtual communication, which replaced the traditional one together with its own language laws and rules; and the role and place of the proverb fund in the new system. In the second part, examples are given, the goals of using the semantically transformed proverb in articles, notes, messages on the sites of the German-speaking Internet space are indicated. The study conducted on the basis of a descriptive method and a selective type of linguistic analysis is based on the concepts, methods and results of modern paremiology and the works of such paremiologists as V.P. Zhukov, V. Mider, H. Walter, V.M. Mokienko. As a result of the analysis, it was concluded that modified proverbs are a rarer phenomenon in virtual space than conventional ones, but more remarkable and interesting from a linguopragmatic point of view. A detailed description and analysis of the methods of modifying the German proverb, as well as its functioning in the context of virtual communication, will help students of the German language to understand more deeply the linguoculturological features of the proverb fund of the language and apply the knowledge gained for successful communication.

Stakeholders’ perspective on the development of a virtual clinic for patients with spinal cord injury: a qualitative study

Purpose This study aims to explore the priorities, preferences, and feedback of multiple healthcare professionals to inform the future development of virtual clinics for community-dwelling adults with spinal cord injury (SCI) in Ontario, Canada. Methods Interpretive description methodology was used to guide our exploration. Semi-structured interviews were conducted with 15 expert healthcare professionals (HCPs) involved in the care of patients with SCI. Interviews were recorded and transcribed verbatim. Interview transcripts were then analyzed using a six-phase thematic analysis approach. Results HCPs perceived virtual care to improve access to care over the long term, particularly to those living in rural areas, as well as increase connections between different providers. However, participants highlighted that in-person care is still required for management of severe SCI-related sequelae that can be life-threatening, such as pressure ulcers, spasticity, respiratory issues, and bowel and bladder complications. Conclusion Our findings can be used to inform policymakers, HCPs, and stakeholders involved with SCI rehabilitation when establishing a virtual clinic for patients with SCI. Results of this study found that policymakers and HCPs should consider hybridized (blend of virtual and in-person) healthcare and uptake of multidisciplinary approaches within the virtual healthcare systems.

Visuomotor predictors of interception.

Intercepting moving targets is a fundamental skill in human behavior, influencing various domains such as sports, gaming, and other activities. In these contexts, precise visual processing and motor control are crucial for adapting and navigating effectively. Nevertheless, there are still some gaps in our understanding of how these elements interact while intercepting a moving target. This study explored the dynamic interplay among eye movements, pupil size, and interceptive hand movements, with visual and motion uncertainty factors. We developed a simple visuomotor task in which participants used a joystick to interact with a computer-controlled dot that moved along two-dimensional trajectories. This virtual system provided the flexibility to manipulate the target's speed and directional uncertainty during chase trials. We then conducted a geometric analysis based on optimal angles for each behavior, enabling us to distinguish between simple tracking and predictive trajectories that anticipate future positions of the moving target. Our results revealed the adoption of a strong interception strategy as participants approached the target. Notably, the onset and amount of optimal interception strategy depended on task parameters, such as the target's speed and frequency of directional changes. Furthermore, eye-tracking data showed that participants continually adjusted their gaze speed and position, continuously adapting to the target's movements. Finally, in successful trials, pupillary responses predicted the amount of optimal interception strategy while exhibiting an inverse relationship in trials without collisions. These findings reveal key interactions among visuomotor parameters that are crucial for solving complex interception tasks.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.

Virtual Teacher-Aided Learning System Based on Voice Operated Character Animation

Virtual Teacher-Aided Learning System Based on Voice Operated Character Animation

Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

Exploring Innovative Methods in Maritime Simulation: A Ship Path Planning System Utilizing Virtual Reality and Numerical Simulation

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs.