System Platform Research Articles

Automobile exhaust flexible thermoelectric harvester enabled by liquid metal-based heatsink

The softening thermoelectric generator based on a deformable heatsink is expected to facilitate achieving remarkable temperature differences and thermal harvesting capabilities while alleviating exhaust back pressure and engine operating perturbations. However, the available soft polymer materials with poor thermal characteristics face challenges in realizing efficient heat transfer. Herein, we reported a highly integrated automobile exhaust flexible thermoelectric generator (IAE-FTEG) enabled by a liquid metal (LM)-based stretchable heatsink for cylindrical thermal sources to achieve efficiently harnessing waste heat energy and continuously powering multiple vehicle-mounted sensors. IAE-FTEG exhibits excellent flexibility (the bending radius at the apex of 0.57 cm) and outstanding heat transfer capability by introducing the porous sandwich-based soft electrode films and LM-based thermal interface material, achieving an output power enhancement of 25.7 %. The flexible heatsink is prepared by embedding LM droplets and copper particles into the elastic matrix forming solid–liquid multiphase composites, which obtain considerable thermal conductivity (2.40 W/mK@200 % stretching deformation) and excellent geometric conformity. We built the bench and the real vehicle test platform of the IAE-FTEG waste heat recovery system. The simulation test results demonstrate that the maximum power density achieved by the IAE-FTEG waste heat recovery system is 244 kW/m3. The real vehicle tests show that the IAE-FTEG waste heat recovery system has a relatively stable output performance with an average power density of 117 kW/m3 under urban conditions and can realize a stable power supply for back-end loads. Our IAE-FTEG can offer new opportunities in enabling efficient curved-surface waste heat recovery, flexible wearable electronics, and personalized thermal management.

Carbon-based phase change composites with directional high thermal conductivity for interface thermal management

Phase change materials (PCMs) can provide a buffer platform for thermal management systems to deal with thermal runaway problems. However, prompt heat dissipation and cost-effective thermal regulation in desired devices are still limited by insufficient thermal transport behavior and liquid leakage of PCMs. Here, we report a facile strategy to develop high-power-density device by carbon-based phase change composites (PCCs) with directional high thermal conductivity. The synergistic effect of self-assembled aligned graphite nanoplatelets (GNPs) inside the resultant PCCs on thermal transport and structural reinforcement enable the PCCs to show high thermal conductivity (in-plane KPCCs of up to 32.86 W m−1 K−1), large heat storage density (158.2–248.3 J g−1), thermal cycling stability, and leakage-proof properties. Furthermore, a PCC-based high-power-density energy device is demonstrated for the tunable thermal regulation by coordinating the orientation of GNPs heat conduction skeleton inside the PCCs with the thermal transport direction of device. The PCC-based module maintains a suitable temperature range of 149–151 °C under various operating conditions and exhibits a decrease of ∼10 °C in the surface peak temperature of power device, showing excellent temperature control performance. Our work holds promising application prospects in the engineering scalable functional PCCs for thermal regulation of electronics and interface thermal management.

Observer-based adaptive fuzzy control of stabilized platform in rotary steerable system with input saturation and output constraint

This paper explores the control strategy for toolface tracking in the stabilized platform of a rotary steerable system, considering both input saturation and output constraint. To mitigate the impact of unknown friction torque and modeling error on the stabilized platform, we propose an adaptive fuzzy backstepping control approach. A fuzzy state observer is specifically devised to handle the uncertain state arising from unknown friction torque and modeling errors. Introducing an auxiliary system effectively compensates for input saturation, and the resolution of the output error constraint is achieved through the construction of a barrier Lyapunov function. Furthermore, employing the Lyapunov method establishes that all signals in the entire closed-loop control system are semi-globally uniformly ultimately bounded. Simulation results confirm the efficacy of the proposed control methodology.

Design of Carbon Monitoring System Platform for Agricultural Energy Internet

As global climate change and carbon emission issues become increasingly severe, the agricultural energy internet plays a crucial role in achieving low-carbon development. The merging of new energy with modern agriculture is progressively being realized, making effective carbon monitoring and accounting especially critical. This paper presents a novel design for a carbon monitoring system platform for the agricultural energy internet, using aquaponics greenhouses as the research object. Addressing the difficulties presented by the diversity and magnitude of agricultural carbon sources, the paper proposes a carbon accounting model applicable to such greenhouse environments. This model aims to enhance energy utilization efficiency and reduce carbon emissions through precise accounting and monitoring. Subsequently, a carbon monitoring system applicable to the agricultural energy internet is designed. This system enables real-time and accurate monitoring and assessment of carbon emissions and absorption in agricultural parks. The platform, developed using a B/S architecture with a separation model between front-end and back-end employing Vue and Spring Boot frameworks, provides a comprehensive solution for monitoring and managing carbon emissions in agricultural energy.

SYSTCM: A systemic web platform for objective identification of pharmacological effects based on interplay of “traditional Chinese Medicine-components-targets”

Mechanism analysis is essential for the use and promotion of Traditional Chinese Medicine (TCM). Traditional methods of network analysis relying on expert experience lack an explanatory framework, prompting the application of deep learning and machine learning for objective identification of TCM pharmacological effects. A dataset was used to construct an interacted network graph between 424 molecular descriptors and 465 pharmacological targets to represent the relationship between components and pharmacological effects. Subsequently, the optimal identification model of pharmacological effects (IPE) was established through convolution neural networks of GoogLeNet structure. The AUC values are greater than 0.8, MCC values are greater than 0.7, and ACC values are greater than 0.85 across various test datasets. Subsequently, 18 recognition models of TCM efficacy (RTE) were created using support vector machines (SVM). Integration of pharmacological effects and efficacies led to the development of the systemic web platform for identification of pharmacological effects (SYSTCM). The platform, comprising 70,961 terms, including 636 Traditional Chinese Medicines (TCMs), 8190 components, 40 pharmacological effects, and 18 efficacies. Through the SYSTCM platform, (1) Total 100 components were predicted from TCMs with anti-inflammatory pharmacological effects. (2) The pharmacological effects of complete constituents were predicted from Coptidis Rhizoma (Huang Lian). (3) The principal components, pharmacological effects, and efficacies were elucidated from Salviae Miltiorrhizae radix et rhizome (Dan Shen). SYSTCM addresses subjectivity in pharmacological effect determination, offering a potential avenue for advancing TCM drug development and clinical applications. Access SYSTCM at http://systcm.cn.

Detrimental Increase of Spin-Phonon Coupling in Molecular Qubits on Substrates.

Molecular qubits are a promising platform for quantum information systems. Although single molecule and ensemble studies have assessed the performance of S = 1/2 molecules, it is understood that to function in devices, regular arrays of addressable qubits supported by a substrate are needed. The substrate imposes mechanical and electronic boundary conditions on the molecule; however, the impact of these effects on spin-lattice relaxation times is not well understood. Here we perform electronic structure calculations to assess the effects of a graphene (Cgr) substrate on the molecular qubit copper phthalocyanine (CuPc). We use a progressive Hessian approach to efficiently calculate and separate the substrate contributions. We also use a simple thermal model to predict the impact of these changes on the spin-phonon coupling from 0 to 200 K. Further analysis of the individual vibrational modes with and without Cgr shows that an overall increase in SPC between the vibrations modes of CuPc with the surface reduces the spin-lattice relaxation time T1. We explain these changes by examining how the substrate lifts symmetries of CuPc in the absorbed configuration. Our work shows that a surface can have a large unintentional impact on SPC and that ways to reduce this coupling need to be found to fully exploit arrays of molecular qubits in device architectures.

Investigating thermal properties of 2D non-layered material using a NEMS-based 2-DOF approach towards ultrahigh-performance bolometer

ABSTRACT Two-dimensional (2D) non-layered materials in many aspects differ from their layered counterparts, and the exploration of their physical properties has produced many intriguing findings. However, due to challenges in applying existing experimental techniques to such nanoscale samples, their thermal properties have remained largely uncharacterized, hindering further exploration and device application using this promising material system. Here, we demonstrate an experimental study of thermal conduction in β-In2S3, a typical non-layered 2D material, using a resonant nanoelectromechanical systems (NEMS) platform. We devise a new two-degrees-of-freedom technique, more responsive and sensitive than Raman spectroscopy, to simultaneously determine both the thermal conductivity to be 3.7 W m−1 K−1 and its interfacial thermal conductance with SiO2 as 6.4 MW m−2 K−1. Leveraging such unique thermal properties, we further demonstrate a record-high power-to-frequency responsivity of −447 ppm/μW in β-In2S3 NEMS sensors, the best among drumhead NEMS-based bolometers. Our findings offer an effective approach for studying thermal properties and exploring potential thermal applications of 2D non-layered materials.

Microbial Composition of Extracted Dental Alveoli in Dogs with Advanced Periodontitis.

Periodontitis is a serious gum infection that damages the soft tissue and destroys the bone supporting the teeth. The aim of the study was to investigate the microbiota using traditional microbiology plating and metagenomic sequencing of extracted tooth alveoli in dogs with severe periodontitis. Isolation of culturable microorganisms was performed as part of bacteriological testing to provide bacteriological diagnosis to veterinary surgeons. Metagenomic sequencing was performed using shotgun sequencing on the Illumina HiSeq system platform. The most prevalent species at sites of periodontal infection detected by metagenomic sequencing were Porphyromonas gulae, Prevotella spp., Tannerella forsythia, Porphyromonas crevioricanis, Porphyromonas cangingivalis, and Bacteroides heparinolyticus. Pasteurella, Streptococcus, and Neisseria were the most frequently isolated culturable bacteria from infected sites detected by traditional microbiologic methods. Metagenomic data revealed that these three genera accounted for only 1.6% of all microbiota at the sites of infection. Antimicrobial resistance patterns of the isolated bacteria included resistance to ampicillin, doxycycline, sulfamethoxazole-trimethoprim, ciprofloxacin, colistin, cefotaxime, and chloramphenicol. Antimicrobial-resistant genes detected using shotgun sequencing also showed resistance to aminoglycosides and macrolides. Dogs with periodontal infections carry bacteria that can cause bite infections in humans as well as multi-resistant isolates. Therefore, treatment and prophylaxis or periodontal disease of dogs is important from a One Health perspective.

Construction and Innovation of Management Mode of Laboratory in Regional and Local Universities

University laboratory is an important base for training students' innovative practice ability and teachers' scientific and technological innovation, and is an important part of university. Traditional laboratory management methods have been unable to adapt to modern practical teaching and scientific and technological innovation. Based on the comprehensive analysis of laboratory management in local colleges and universities and the actual situation of existing laboratories, a comprehensive laboratory management system was developed, which consisted of six functional modules: large-scale equipment sharing platform, instrument and equipment maintenance system, experimental consumables management system, laboratory and practice teaching integrated service platform and laboratory safety management and service platform. The system fully integrates and displays the laboratory teaching resources, realizes the open self-management mode, provides the convenience of experimental teaching, and effectively improves the efficiency and management level of the laboratory.

Variability in sleep architecture and alterations in circadian rhythms in patients with acute cerebral infarction accompanied by sleep-disordered breathing.

To continuously and dynamically monitor the sleep status of patients in the acute phase of cerebral infarction, and to investigate the characteristics of acute cerebral infarction(ACI)associated with sleep-disordered breathing (SDB), variations in sleep structure, and changes in sleep circadian rhythms. Patients with ACI within 48h of onset who were admitted to the Department of Neurology at Kailuan General Hospital from November 2020 to December 2022 were selected. Detailed baseline information such as age, gender, smoking history, drinking history, were recorded for the selected participants. From the beginning of their hospitalization, the selected participants were monitored for their sleep status continuously for 5 days using the Intelligent Mattress-based Sleep Monitoring Platform System(IMSMPS). Based on the heart rate data obtained from the monitoring, the interdaily stability (IS) and intradaily variability (IV) of the sleep circadian rhythm were calculated. 1,367 patients with ACI were selected. Monitoring results over 5 days indicated 147 cases (10.75%) without SDB, and 1,220 cases (89.25%) with SDB. Among the group with SDB, there were 248 cases (18.14%) with continuous mild SDB, 395 cases (28.90%) with moderate SDB, 295 cases (21.58%) with severe SDB, and 282 cases (20.63%) that fluctuated between different severity levels. Within this fluctuating group, 152 cases (53.90%) fluctuated between two severity levels, 120 cases (42.55%) between three levels, and 10 cases (3.55%) among all four levels. There were statistically significant differences (P < 0.05) in the sleep latency, sleep efficiency, non-rapid eye movement stages 1-2, rapid eye movement, proportion of non-rapid eye movement, proportion of rapid eye movement, wake after sleep onset, time out of bed, number of awakenings, respiratory variability index, and heart rate variability index among patients with ACI monitored from day 1 to 5. However, other monitored sleep structure parameters did not show statistically significant differences (P > 0.05). The coefficient of variation for all sleep monitoring parameters ranged between 14.54 and 36.57%. The IV in the SDB group was higher than in the group without SDB (P < 0.05), and the IS was lower than in the group without SDB (P < 0.05). Patients in the acute phase of cerebral infarction have a high probability of accompanying SDB. The sleep structure of these patients shows significant variability based on the onset time of the stroke, and some patients experience fluctuations among different severity levels of SDB. ACI accompanied by SDB can further reduce the IS of a patient's sleep circadian rhythm and increase its IV.

Working characteristic research on the mechanical stable platform of the automatic vertical drilling system

Working characteristic research on the mechanical stable platform of the automatic vertical drilling system

Efficient pathway-driven scyllo-inositol production from myo-inositol using thermophilic cells and mesophilic inositol dehydrogenases: a novel strategy for pathway control.

Enzyme cocktails are commonly employed for cell-free chemical synthesis; however, their preparation involves cumbersome processes. This study affirms that mesophilic enzymes in thermophilic crude extracts can function as specific catalysts at moderate temperatures, akin to enzyme cocktails. The catalyst was prepared by permeabilizing cells without the need for concentration, extraction, or purification processes; hence, its preparation was considerably simpler compared with conventional methods for enzyme cocktails. This approach was employed to produce pure scyllo-inositol from an economical substrate. Notably, this marks the first large-scale preparation of pure scyllo-inositol, holding potential pharmaceutical significance as scyllo-inositol serves as a promising agent for certain diseases but is currently expensive. Moreover, this approach holds promise for application in pathway engineering within living cells. The envisioned pathway is designed without chromosomal modification and is simply regulated by switching culture temperatures. Consequently, this study introduces a novel platform for both whole-cell and cell-free synthetic systems.

Low Costs Electrical Calibration System of SLM with the Uncertainty Measurements Compared with Primary System Platform Brūel &amp; Kjær type 3630

Sound level meter (SLM) calibration according to IEC61672-1, 2, 3: 2013 is an essential step in the measurement process. It ensures that the SLM meets its specifications. There is a strong need for an efficient and cheap calibration system device for the electrical calibration of SLM since it avoids the body effect and angle sensitivity of the microphone membrane to the sound incidence angle for free field calibration. As well as, it avoids leakage errors due to the microphone fitting in the coupler and the coupler effect in pressure field calibration. This work gives an alternative system device used in the electrical calibration of SLM. This system consists of individual tools; PULSE signal generator, universal frequency counter, HP Dynamic signal analyzer, and DMM voltmeter. All system tools are complying with the IEC17025. The calibration procedures follow the second edition of IEC61672-2. Calibration measurements of frequency, time-weightings, and linearity of range parameters as the major features of SLMs are tested in the frequency range of 31.5 Hz to 16 k Hz. The calibration was repeated 5 times on different days and different warm-up times for devices to estimate the associated uncertainty measurement values in accordance with Part 2 and Part 3 of IEC 61672. The obtained results show that, the calibration measurement deviations are in the allowable tolerance of the IEC 61672-1 and they are comparable with that obtained from the primary Brüel &amp; Kjær (B &amp; K) platform 3630. The computed uncertainty of measurements is in accepted limits that are required by IEC 61672-1 which is determined to the level of confidence of 95%, using coverage factor 2.

Experience in the development and use of simulators for professional training of specialists in the electric power industry

Issues of development of simulators for training and testing of electrical engineers and students of electrical engineering specialties were considered. They are based on modern knowledge in the field of electricity. The main characteristics of simulators and requirements for their computer support are given. The developments implement a new method of practical training and testing of electrical engineers and can be used at all stages of electrical engineer training. The considered simulators are based on a combination of practical and theoretical knowledge of the electric power industry. The purpose of the developments is to activate and increase the efficiency of the educational process due to the introduction of new learning technologies. The technical capabilities of the software implement both individual and team work of students and electric power personnel. It was noted that in recent years, two professional simulators, implemented in the form of two laboratory workshops, have been developed and implemented in the educational process at the department of electrical energy transmission of National technical university “Kharkiv polytechnic institute”. The first helps students study the main provisions of the Normative Documentation in the power industry, the second – the rules and sequence of operational switching in electrical networks. In addition, experts determine the high efficiency of training systems based on precedent-analytical models. The exercise program according to the plan-do-check-act cycle is considered. Its basis is the well-known concept of continuous quality improvement “Plan-Do-Check-Act” (Deming's cycle). For the electric power industry, a plan-do-check-act cycle approach can consist of cyclical planning of activities, implementation of planned actions, verification of results obtained and correction of non-conformance to expectations. The precedent-scenario method of constructing a training system based on the software platform of the training system based on the plan-do-check-act scenario is presented in detail.

Innovative BIM technology application in the construction management of highway

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Wideband coherent microwave conversion via magnon nonlinearity in a hybrid quantum system

Frequency conversion is a widely realized physical process in nonlinear systems of optics and electronics. As an emerging nonlinear platform, spintronic devices have the potential to achieve stronger frequency conversion. Here, we demonstrated a microwave frequency conversion method in a hybrid quantum system, integrating nitrogen-vacancy centers in diamond with magnetic thin film CoFeB. We achieve a conversion bandwidth ranging from 0.1 to 12 GHz, presenting an up to 25th order frequency conversion and further display the application of this method for frequency detection and qubits coherent control. Distinct from traditional frequency conversion techniques based on nonlinear electric response, our approach employs nonlinear magnetic response in spintronic devices. The nonlinearity, originating from the symmetry breaking such as domain walls in magnetic films, presents that our method can be adapted to hybrid systems of other spintronic devices and spin qubits, expanding the application scope of spintronic devices and providing a promising on-chip platform for coupling quantum systems.

Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

Truly Modular and Open System Design is Difficult

AbstractIn the United States, major defense acquisition programs must implement a modular open systems approach (MOSA) as required by U.S. law. (Defense Standardization Program, 2016). Some in the defense community have focused on MOSA as a checklist compliance activity. However, designing economically and operationally competitive system platforms that are truly modular is extremely challenging. Many such modular system platform efforts fail to meet early expectations for convenience, cost, and community uptake. Open standards are challenging as well. If a highly successful, stable, well‐documented, appropriate open standard is available for an interface, then leveraging such a standard can be fairly straightforward. However, often there is no obvious or adequate open standard available and the system platform owners have to organize a community to bring such a standard into existence.This paper will review three concrete examples of such MOSA efforts. The three cases cover both commercial and defense applications as well as covering both hardware and software. The two systems that focused on hardware modularity did eventually go into production but did not completely fulfill early expectations for their programs. The software system used an out‐of‐the‐box approach and achieved a best‐in‐class result in terms of economic effectiveness, time to market, and stakeholder satisfaction. This paper will discuss underlying challenges that seem to be common to all such efforts, review the results of the three cases, and offer some simple guidelines for increasing the probability of success for such a program.

Deep reinforcement learning attitude control of stabilized platform for rotary steerable system based on extended state observer

A Deep Deterministic Policy Gradient with Extended State Observer (DDPG_ESO) method is proposed to address control accuracy and robustness issues in the attitude control of stabilized platforms for rotary steerable systems. A nonlinear extended state observer is developed to estimate the total system disturbance, which is then integrated into a deep reinforcement learning framework to enhance the controller’s adaptive capabilities. The controller is trained using variable amplitude and frequency external disturbances to improve its ability to suppress these disturbances. The superiority of the DDPG_ESO controller is validated through simulation experiments, demonstrating rapid tracking of toolface angles, reduced overshoot, and maintained steady-state errors. Compared to Proportional Integral Derivative (PID), Active Disturbance Rejection Control (ADRC), and Deep Deterministic Policy Gradient (DDPG) controllers, the DDPG_ESO algorithm exhibits higher robustness and adaptability under varying operational conditions, effectively mitigating the impact of external disturbances and modeling errors. The results indicate potential for practical application in the drilling industry

Using Immersive Virtual Reality to Classify Pediatric Thoracolumbar Spine Injuries.

Objective This study aimed to assess the reliability and reproducibility of the AO Spine Thoracolumbar Injury Classification System by using virtual reality (VR). We hypothesized that VR is a highly reliable and reproducible method to classify traumatic spine injuries. Methods VR 3D models were created from CT scans of 26 pediatric patients with thoracolumbar spine injuries. Seven orthopedic trainees were educated on the VR platform and AO Spine Thoracolumbar Injury Classification System. Classifications were summarized by primary class and subclass for both rater readings performed two weeks apart with image order randomized. Intra-observer reproducibility was quantified by Fleiss's kappa (kF) for primary classifications and Krippendorff's alpha (aK) for subclassifications along with 95% confidence intervals (CIs) for each rater and across all raters. Inter-observer reliability was quantified by kFfor primary classifications and aKfor subclassifications along with 95% CIs across all raters for the first read, the second read, and all reads combined. The interpretations were as follows: 0-0.2: slight; 0.2-0.4: fair; 0.4-0.6: moderate; 0.6-0.8: substantial; and >0.8: almost perfect agreement. Results A total of 364 classifications were submitted by seven raters. Intra-observer reproducibility ranged from moderate (kF=0.55) to almost perfect (kF=0.94) for primary classifications and from substantial (aK=0.68) to almost perfect (aK=0.91) for subclassifications. Reproducibility was substantial across all raters for the primary class (kF=0.71; 95% CI=0.61-9.82) and subclass (aK=0.79; 95% CI=0.69-0.86). Inter-observer reliability was substantial (kF=0.63; 95% CI=0.57-0.69) for the first read, moderate (kF=0.58; 95% CI=0.52-0.64) for the second read, and substantial (kF=0.61; 95% CI=0.56-0.65) for all reads for primary classifications. For subclassifications, inter-observer reliability was substantial (aK=0.74; 95% CI=0.58-0.83) for the first read, second read (aK=0.70; 95% CI=0.53-0.80), and all reads (aK=0.72; 95% CI=0.60-0.79). Conclusions Based on our findings, VR is a reliable and reproducible method for the classification of pediatric spine trauma, besides its ability to function as an educational tool for trainees. Further research is needed to evaluate its application for other spine conditions.