Update Time Research Articles

Twelve tips for medical educators to optimize their curricula vitae

A good curriculum vitae (CV) highlights medical educators’ academic achievements and supports their professional goals. Many faculty struggle with timely updates and strategic formatting. These twelve tips will help medical educators optimize their CV to best showcase their strengths and accomplishments. The first three tips outline a process: identify a system to collect potential entries and schedule regular time for updates. Tips four and five detail how to tailor traditional CV formatting to best describe the work of medical educators. The next few tips offer concrete strategies and examples of CV entries to consider for inclusion. The remaining tips remind faculty to ask for help from colleagues, who can share a sample CV and identify overlooked activities. Our intention is to transform a task that can be burdensome into a process that seamlessly captures the breadth of our work as medical educators and allows for introspection and growth.

Novel pharmacological developments in the management of paediatric inflammatory bowel disease: Time for guideline update - A narrative review.

The incidence of paediatric inflammatory bowel disease (IBD) continues to increase in both adults and children across the globe, with more than one third of the patients not responding to anti-tumour necrosis factor biologics and immune modulators. This narrative review provides an overview of novel pharmacological developments in the management of paediatric IBD, including new biological therapies. A PubMed Medline search was performed to include randomised controlled trials, retrospective and prospective observational studies, and relevant case reports of children with IBD published between 2018 and January 2023. Guidelines and protocols from relevant paediatric and adult gastroenterology societies, such as the European Society for Paediatric Gastroenterology, Hepatology and Nutrition and the European Crohn's and Colitis Organisation, were also included. Non-pharmacological treatments including therapeutic diets and faecal microbiota transplantation were outside the scope of this work. Early real-world evidence suggests that newer biologics and small molecules, such as anti-integrins, interleukin-12 and/or interleukin-23 inhibitors, Janus kinase and signal transducer and activator of transcription proteins inhibitors, are safe and effective in adult patients with IBD, with promising growing evidence for paediatric IBD. While many developments have been achieved with novel pharmacological treatments to manage IBD, ongoing research is required to confirm their effectiveness and safety in the paediatric age. Extending the licence of novel treatments to children will be crucial to tackle the increasing loss of response to conventional treatments. International guidelines will require timely updating to incorporate novel treatments within the existing protocols.

Do children have a right to do nothing? Exploring the place of passive leisure in Australian school age care

In 2021, the Australian Government commissioned a review and update of My Time Our Place, its curriculum framework for School-Age Care services for primary-age children. One update trialled was the introduction of passive leisure. Whilst children’s passive use of leisure time is recognised as a right, it is often problematised and associated with negative health outcomes. This article explores a trial of passive leisure provision. It provides hopeful evidence that passive leisure spaces can be interactive, conversational and restful.

The black widow pulsar J1641+8049 in the optical, radio, and X-rays

ABSTRACT PSR J1641+8049 is a 2 ms black widow pulsar with the 2.2 h orbital period detected in the radio and γ-rays. We performed new phase-resolved multiband photometry of PSR J1641+8049 using the OSIRIS instrument at the Gran Telescopio Canarias. The obtained data were analysed together with the new radio-timing observations from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), the X-ray data from the Spectrum-RG/eROSITA all-sky survey, and all available optical photometric observations. An updated timing solution based on CHIME data is presented, which accounts for secular and periodic modulations in pulse dispersion. The system parameters obtained through the light-curve analysis, including the distance to the source 4.6–4.8 kpc and the orbital inclination 56–59 deg, are found to be consistent with previous studies. However, the optical flux of the source at the maximum brightness phase faded by a factor of ∼2 as compared to previous observations. Nevertheless, the face of the J1641+8049 companion remains one of the most heated (8000–9500 K) by a pulsar among the known black widow pulsars. We also report a new estimation on the pulsar proper motion of ≈2 mas yr−1, which yields a spin-down luminosity of ≈4.87 × 1034 erg s−1 and a corresponding heating efficiency of the companion by the pulsar of 0.3–0.7. The pulsar was not detected in X-rays implying its X-ray-luminosity was $\lesssim$3 × 1031 erg s−1 at the date of observations.

Intelligent Traffic Monitoring System Using Vehicular Ad Hoc Network

The growing significance of road safety and human engagement in transport has emerged as a matter of national concern, exerting a profound impact on the lives of individuals.. Many road accidents and crashes failed to ensure human life safety. As a result, the traffic management system must maintain the balance in accordance with the maximum road limits. Vehicles with sensors and automated self-driving capabilities are now available, such as Tesla and others. The proposed system is based on a technique known as Intervention linear minimum spanning tree (ILMST), which employs a topology with lengths that are proportionally equal. When using dynamic topology, there is packet loss during a change of location or a continuous update in networking via vehicle movement from one location to another. In this manner, each node computes the weighted nodes with a number of partitions in order to provide a linear time update. This reduces the number of connected edges in the graph that are repeated. When the size of the repeated graphs that relate the GPS route from the maps is reduced, traffic updates avoid recursion and provide the best routes for customers. Traffic congestion overhead can be reduced by implementing the proposed methodology. It is possible to avoid it where there are traffic signals and all other sensor-based wireless devices in a vehicular Ad Hoc Network (VANET). The safety measures are also a necessary step based on the communications in routing and other protocols. The system, when combined with a neural network-based positioning system (NNPS) with various perceptrons, can maintain vehicle speed and categorize safety threats such as group classification. A solution can be found by repairing the DDoS attack based on the results of the various aspects that provide output for malicious monitoring.

Do natural resources ensure energy efficiency? A novel paradigm of resources-efficiency nexus for sustainable development

There is been ample discussion on the resource-growth and resource-finance nexus in almost every country but the resource-energy paradigm is lacking in developed and developing economies. Henceforth, this research inspects the link between natural resources and energy efficiency in the USA from 1985 to 2021. To avoid biases in the outputs, the research also included the role of economic growth (GDP) and renewable energy(REC). The research utilized an updated time series data sample and also employed relevant approaches which comprise DF-GLS for unit root determination, the Bayer-Hanck test for equilibrium, and the novel MMQR as the primary approach. For robustness tests, the research included parametric tests such as FMOLS, DOLS, CCR, and a non-parametric approach which is BSQR for validity. The results observed that variables are stationary at Difference D(1) and long-term equilibrium has been found. Moreover, the main outcomes disclose that natural resources have an asymmetric relationship where the forest rents present a resource curse while mineral rents have blessings for energy efficiency in the USA across all quantiles. Moreover, GDP and REC substantially improve energy efficiency in the USA. The robustness protocols exhibit that outcomes are analogous to the main outcomes and are therefore, valid, reliable, and robust. Established on the estimates, the research provides relevant policy suggestions to policymakers concerning the sustainable use of natural resources, economic growth, and renewable energy related to energy efficiency.

An adaptive event-triggered quantized secondary regulation strategy for microgrids with actuator faults

This paper focuses on the event-triggered quantized secondary regulation problem for islanded alternating-current (AC) microgrids with actuator faults. In order to handle the actuator faults, including bias faults and partial loss of effectiveness faults, a distributed secondary regulation strategy is proposed, which achieves voltage and frequency regulations, as well as power sharing. Due to the limited communication bandwidth, a uniform quantizer is introduced for information transmission. Meanwhile, to save system resources and relieve the communication burden, an adaptive event-triggered mechanism is designed. Finally, the proposed strategy is validated by using an islanded AC microgrid test system built in MATLAB/SimPowerSystems, which indicates that the proposed strategy increases the reliability of system and reduces the controller update times.

The Swiss Federal Supreme Court’s Fire Ring Decision and the Evaluation of Works of Applied Art Under Copyright Law – A Burning Topic

Abstract In its most recent Fire Ring decision, the Swiss Federal Supreme Court had to rule on the copyright protection of works of applied art for the first time in several years. Works of applied art are included in the Swiss Copyright Act in Art. 2(2)(f) as a separate category of protected works, distinguished from other categories of works because they serve a utilitarian purpose and combine practical applicability with an additional aesthetic appeal. In light of the most recent CJEU decisions regarding copyright protection of works of applied art and industrial design, specifically its decisions in Brompton (GRUR International 2020, 969) and Cofemel (GRUR International 2020, 322) the Swiss Federal Supreme Court had the opportunity – not being directly bound by Union law – to position itself with regard to EU judicial practice and at the same time update its own sparse and partly outdated case law. However, it clearly failed to do so, not only from a legal-theoretical perspective but also from a case-specific perspective, and this raises some additional questions that deserve to be addressed.

Minimizing age of information under arbitrary arrival model with arbitrary packet size

We consider a single source–destination pair, where information updates (in short, updates) arrive at the source at arbitrary time instants. For each update, its size, i.e. the service time required for complete transmission to the destination, is also arbitrary. At any time, the source may choose which update to transmit, while incurring transmission cost that is proportional to the duration of transmission. We consider the age of information (AoI) metric that quantifies the staleness of the update (information) at the destination. At any time, AoI is equal to the difference between the current time, and the arrival time of the latest update (at the source) that has been completely transmitted (to the destination). The goal is to find a causal (i.e. online) scheduling policy that minimizes the sum of the AoI and the transmission cost, where the possible decisions at any time are (i) whether to preempt the update under transmission upon arrival of a new update, and (ii) if no update is under transmission, then choose which update to transmit (among the available updates). In this paper, we propose a causal policy called SRPT+ that at each time, (i) preempts the update under transmission if a new update arrives with a smaller size (compared to the remaining size of the update under transmission), and (ii) if no update is under transmission, then from the set of available updates with size less than a threshold (which is a function of the transmission cost and the current AoI), begins to transmit the update for which the ratio of the reduction in AoI upon complete transmission (if not preempted in future) and the remaining size, is maximum. We characterize the performance of SRPT+ using a metric called the competitive ratio, i.e. the ratio of the cost of causal policy and the cost of an optimal offline policy (that knows the entire input in advance), maximized over all possible inputs. We show that the competitive ratio of SRPT+ is at most 5. In the special case when there is no transmission cost, we further show that the competitive ratio of SRPT+ is at most 3.

Workflow Considerations for Biology-Guided Radiotherapy (BgRT) Implementation.

Biology-guided radiotherapy (BgRT) is a novel platform that combines real-time PET imaging with a 6MV Linac to target tumors. The performance and safety of BgRT was assessed in the BIOGUIDE-X clinical trial. This study aims to report on the BgRT workflow steps and assess the time required for each step of the BgRT process during this trial. A total of nine patients were enrolled in the second Cohort of the BIOGUIDE-X study which included patients treated with stereotactic body radiotherapy (SBRT) for lung tumors (5) and bone tumors (4). The pre-treatment BgRT workflow includes CT simulation, contouring, imaging-only (BgRT Modeling) PET acquisition, BgRT planning, patient specific QA and plan approval. The imaging-only PET acquisition on the X1 collects a representative PET volumetric 3D image and is an input to develop the BgRT treatment plan. The steps during the BgRT delivery session are kVCT localization, PET pre-scan, PET evaluation and BgRT delivery. The PET PreScan is a 1-pass short-duration PET acquisition that is used to confirm that the PET biodistribution on the day of treatment is consistent with that of the imaging-only PET. During BIOGUIDE-X, the BgRT delivery step was replaced by a 4-pass long-PET acquisition that was used to emulate the expected BgRT dose distribution without turning the beam on. To assess BgRT workflow, times from 18F-FDG injection to image-only PET acquisition, 18F-FDG injection to PET pre-scan, Pre-scan to PET evaluation, and PET evaluation to BgRT delivery (long PET acquisition) were recorded. Time between the 18F-FDG injection and the X1 imaging-only PET scan was 84 ± 19 minutes which includes time for 18F-FDG update. Average time to perform imaging-only PET scan was 26 ± 4 minutes. During the BgRT 'delivery' session, the mean time between the kVCT acquisition and PET pre-scan acquisition was 7 ± 3 minutes. The mean time to acquire a 1-pass PET pre-scan was 6 ± 1 then followed by 6 ± 1 minutes for the PET pre-scan dose calculation to estimate the BgRT doses that it would have delivered for this fraction. On average, the PET reconstruction, the PET signal localization verification and the evaluation of safety metrics took 11 ± 4 minutes. The mean time for BgRT 'delivery' was 27 ± 5 minutes based on the 4-pass long PET acquisition. Time from the start of the BgRT session to the end of the BgRT 'delivery' with this version of the investigative product release was 65 ± 9 minutes. The new processes introduced by the BgRT technology were evaluated and found clinically feasible. Improvements are being undertaken to shorten the time required for each step and to increase patient comfort ahead of BgRT clinical implementation.

Flood Risk Mapping and the Distributional Impacts of Climate Information

This paper examines the provision of official flood risk information in the United States and its distributional impacts on residential flood insurance take-up. Assembling new data on all flood maps produced after Hurricane Katrina, I first document that updated maps decreased the number of properties zoned in high-risk floodplains and incorrectly omitted five million properties that should have been rezoned inside these areas. Removals and omissions disproportionately occurred in neighborhoods with more Black and Hispanic residents. Leveraging the staggered timing of map updates, I estimate they decreased flood insurance take-up and exacerbated racial disparities in insurance coverage. These effects are driven by beliefs about risks and regulatory constraints. Correcting flood maps could increase welfare by $20 billion annually, but past updates decreased welfare due to distorted risk and price signals.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

Performance analysis of databases created in virtualized and containerized environment

Database systems are currently implemented on different environments - containerized and virtualized. This paper aimed to compare the performance of relational databases running on these two environments. Therefore, two research hypotheses were formulated. The first hypothesis assumed that databases running on Docker containers are more efficient than those on virtual machines. The second one assumed that the Oracle is the most efficient database regardless of which environment it was run on. MySQL, PostgreSQL, Microsoft SQL Server and Oracle databases were tested. The study measured the execution times of INSERT, UPDATE, DELETE and SELECT queries. Each test was repeated 100 times. It was stated that databases running on Docker containers outperform instances running on virtual machines. Furthermore, it was found that the PostgreSQL database have a definite advantage in performance over the rest, analysed databases, which allowed the second hypothesis to be rejected.

A census of rotating radio transients at 150 MHz with the Irish LOFAR station

ABSTRACT Rotating radio transients (RRATs) are neutron stars that emit detectable radio bursts sporadically. They are statistically distinct in the neutron star population, in many observable properties, but by their nature are practically difficult to study in depth. In this paper, we present the results from 1408 h of observations of RRAT candidates using the Irish station of the Low Frequency Array (LOFAR) at 150 MHz. As of October 2022, this census involved observing 113 sources, leading to 29 detections which were then followed up systematically. Single-pulse emission was detected from 25 sources, and periodic emission from 14 sources. Eighteen sources were found to have emission behaviour that is not discussed in prior works using LOFAR instruments. Four novel or modified source periods have been determined, ranging from 1.5 to 3.9 s, and eight new or updated phase-coherent pulsar timing ephemerides have been produced using detected bursts. One unexpected single-pulse with a clearly-Galactic dispersion measure was detected as a part of this work but has not been re-detected in follow-up observations. Observations are ongoing to expand the number of observed sources and further characterize and improve ephemerides for the detected sources. This census has demonstrated the capability for international LOFAR stations to detect, monitor, and characterize a significant fraction of these unique sources.

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

Efficient Dynamic Weighted Set Sampling and Its Extension

Given a weighted set S of n elements, weighted set sampling (WSS) samples an element in S so that each element a i ; is sampled with a probability proportional to its weight w ( a i ). The classic alias method pre-processes an index in O ( n ) time with O ( n ) space and handles WSS with O (1) time. Yet, the alias method does not support dynamic updates. By minor modifications of existing dynamic WSS schemes, it is possible to achieve an expected O (1) update time and draw t independent samples in expected O ( t ) time with linear space, which is theoretically optimal. But such a method is impractical and even slower than a binary search tree-based solution. How to support both efficient sampling and updates in practice is still challenging. Motivated by this, we design BUS , an efficient scheme that handles an update in O (1) amortized time and draws t independent samples in O (log n + t) time with linear space. A natural extension of WSS is the weighted independent range sampling (WIRS) , where each element in S is a data point from R. Given an arbitrary range Q = [ℓ, r ] at query time, WIRS aims to do weighted set sampling on the set S Q of data points falling into range Q. We show that by integrating the theoretically optimal dynamic WSS scheme mentioned above, it can handle an update in O (log n ) time and can draw t independent samples for WIRS in O (log n + t ) time, the same as the state-of-the-art static algorithm. Again, such a solution by integrating the optimal dynamic WSS scheme is still impractical to handle WIRS queries. We further propose WIRS-BUS to integrate BUS to handle WIRS queries, which handles each update in O (log n ) time and draws t independent samples in O (log 2 n + t ) time with linear space. Extensive experiments show that our BUS and WIRS-BUS are efficient for both sampling and updates.

Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m2. Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.

Observer-Based Event-Triggered Adaptive Control for Nonlinear Multiagent Systems With Unknown States and Disturbances.

Based on radial basis function neural networks (RBF NNs) and backstepping techniques, this brief considers the consensus tracking problem for nonlinear semi-strict-feedback multiagent systems with unknown states and disturbances. The adaptive event-triggered control scheme is introduced to decrease the update times of the controller so as to save the limited communication resources. To detect the unknown state, external disturbance, and reduce calculation workload, the state observer and disturbance observer as well as the first-order filter are first jointly constructed. It is shown that all the output signals of followers can uniformly track the reference signal of the leader and all the error signals are uniformly bounded. A simulation example is carried out to further prove the effectiveness of the proposed control scheme.

Deterministic Dynamic Matching in Worst-Case Update Time

We present deterministic algorithms for maintaining a (3/2 + epsilon ) and (2 + epsilon )-approximate maximum matching in a fully dynamic graph with worst-case update times {hat{O}}(sqrt{n}) and {tilde{O}}(1) respectively. The fastest known deterministic worst-case update time algorithms for achieving approximation ratio (2 - delta ) (for any delta > 0) and (2 + epsilon ) were both shown by Roghani et al. (Beating the folklore algorithm for dynamic matching, 2021) with update times O(n^{3/4}) and O_epsilon (sqrt{n}) respectively. We close the gap between worst-case and amortized algorithms for the two approximation ratios as the best deterministic amortized update times for the problem are O_epsilon (sqrt{n}) and {tilde{O}}(1) which were shown in Bernstein and Stein (in: Proceedings of the twenty-seventh annual ACM-SIAM symposium on discrete algorithms, 2016) and Bhattacharya and Kiss (in: 48th international colloquium on automata, languages, and programming, ICALP 2021, 12–16 July, Glasgow, 2021) respectively. The algorithm achieving (3/2 + epsilon ) approximation builds on the EDCS concept introduced by the influential paper of Bernstein and Stein (in: International colloquium on automata, languages, and programming, Springer, Berlin, 2015). Say that H is a (alpha , delta )-approximate matching sparsifier if at all times H satisfies that mu (H) cdot alpha + delta cdot n ge mu (G) (define (alpha , delta )-approximation similarly for matchings). We show how to maintain a locally damaged version of the EDCS which is a (3/2 + epsilon , delta )-approximate matching sparsifier. We further show how to reduce the maintenance of an alpha -approximate maximum matching to the maintenance of an (alpha , delta )-approximate maximum matching building based on an observation of Assadi et al. (in: Proceedings of the twenty-seventh annual (ACM-SIAM) symposium on discrete algorithms, (SODA) 2016, Arlington, VA, USA, January 10–12, 2016). Our reduction requires an update time blow-up of {hat{O}}(1) or {tilde{O}}(1) and is deterministic or randomized against an adaptive adversary respectively. To achieve (2 + epsilon )-approximation we improve on the update time guarantee of an algorithm of Bhattacharya and Kiss (in: 48th International colloquium on automata, languages, and programming, ICALP 2021, 12–16 July, Glasgow, 2021). In order to achieve both results we explicitly state a method implicitly used in Nanongkai and Saranurak (in: Proceedings of the twenty-seventh annual ACM symposium on theory of computing, 2017) and Bernstein et al. (Fully-dynamic graph sparsifiers against an adaptive adversary, 2020) which allows to transform dynamic algorithms capable of processing the input in batches to a dynamic algorithms with worst-case update time.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.