Adaptive Mode Research Articles

Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach

PurposeOnline adaptive radiotherapy (oART) treatment planning requires evaluating the temporal robustness of reference plans, anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the CBCT-guided adaptive workflow and is the first to assess an adaptive specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation. Methods and MaterialsA quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis, and failure mode and effects analysis (FMEA). Fault trees were created for under/overdosing targets, treatment deficiencies, as well as assisted in identifying failure modes for the FMEA. Treatment deficiency was defined to include a non-ideal oART plan resulting in treatment with a lower quality plan (either oART or scheduled plan), treatment delay, or cancelling treatment for the day. A reference planning checklist was created to catch failure modes before reaching the patient. Risk priority numbers (RPN=Severity*Detectability*Occurrence) were scored with and without the reference planning checklist to quantify risk mitigation. A root cause analysis was conducted for an event where an adaptive plan failed to generate. ResultsThe reference planning checklist (with items covering patient background, contouring/planning robustness for anatomy variability, and machine limitations) reduced the RPN for all failure modes. Only 1 failure mode with a risk priority number >150 occurred with the reference planning checklist compared to 29 failure modes without, including 14 adaptive specific failure modes. Contouring, planning, setup, scheduling, and documentation errors were identified during the fault tree analysis. 29/70 errors were adaptive specific. The reference planning checklist could address 23/33 for over/under dosing and 28/37 errors for treatment deficiency. The root cause analysis highlighted the need for checking setup prior to adaptive plan delivery, and time-out checklist. ConclusionsThe reference planning checklist improved detection of the failure modes and improves the quality and robustness of the plans produced for oART. It is ideally performed before physician plan review to prevent last minute re-plan (before or after first adaptive treatment) and delay of patient start. The checklist presented can be modified based on failures specific to individual clinics and utilized at various planning steps based on available resources.

Research on ultrasonic echo signal denoising via integration of adaptive variational mode decomposition algorithm and convolutional neural network

Research on ultrasonic echo signal denoising via integration of adaptive variational mode decomposition algorithm and convolutional neural network

Image Enhancement Using Bidimensional Empirical Mode Decomposition and Morphological Operations for Brain Tumor Detection and Classification.

The three steps of brain image processing – preprocessing, segmentation, and classification are becoming increasingly important in patient care. The aim of this article is to present a proposed method in the mentioned three-steps, with emphasis on the preprocessing step, which includes noise removal and contrast enhancement. The fast and adaptive bidimensional empirical mode decomposition and the anisotropic diffusion equation as well as the modified combination of top-hat and bottom-hat transforms are used for noise reduction and contrast enhancement. Fast C-means clustering with enhanced image is used to detect tumors and the tumor cluster corresponds to the maximum centroid. Finally, Ensemble learning is used for classification. The Figshare brain tumor dataset contains magnetic resonance images used for data selection. The optimal parameters for both noise reduction and contrast enhancement are investigated using a tumor contaminated with Gaussian noise. The results are evaluated against state-of-the-art results and qualitative performance metrics to demonstrate the dominance of the proposed approach. The fast C-means algorithm is applied to detect tumors using twelve enhanced images. The detected tumors were compared to the ground truth and showed an accuracy and specificity of 99% each, and a sensitivity and precision of 90% each. Six statistical features are retrieved from 150 enhanced images using wavelet packet coefficients at level 4 of the Daubechies 4 wavelet function. These features are used to develop the classifier model using ensemble learning to create a model with training and testing accuracy of 96.7% and 76.7%, respectively. When this model is applied to classify twelve detected tumor images, the accuracy is 75%; there are three misclassified images, all of which belong to the pituitary disease group. Based on the research, it appears that the proposed approach could lead to the development of computer-aided diagnosis (CADx) software that physicians can use as a reference for the treatment of rain tumor.

Scientific preparation for JRT: Wind pressure prediction model for large radio telescope based on real data from multi-sensors

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Agent-SwinPyramidNet: an enhanced deep learning model with AMTCF-VMD for anomaly detection in oil and gas pipelines

PurposeThe anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approachFirst, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.FindingsIn the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.Originality/valueThis paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Research on Dance Education in Universities Based on Online and Offline Blended Teaching

With the rapid development of educational informatization, the transformation of teaching methods has become an inevitable requirement for the long-term development of education, which also puts forward higher requirements for the work of art education in universities. With the development of network technology, online and offline hybrid teaching modes came into being. This mode uses the Internet, cloud computing and other technologies to provide students with a diversified and adaptable expansion teaching mode. As a branch of art teaching in universities, college dance should focus on innovative teaching methods, construct a blended online and offline teaching model, broaden educational channels, and enrich educational resources. Based on this, the article analyzes and studies the blended teaching of online and offline dance education in universities, explores the advantages and challenges of blended dance teaching, analyzes the necessity of blended teaching in online and offline dance teaching in universities and proposes specific educational strategies for reference.

Adaptive immune fuzzy quasi-sliding mode control for leader–follower formation of wheeled mobile robots under uncertainties and disturbances with obstacle avoidance

PurposeThis paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.Design/methodology/approachTo deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.FindingsTo verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.Originality/valueThe proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

Transformer and cross-attention-based multi-sensor in-situ monitoring of molten pool stability and part quality in laser powder bed fusion

The fluctuations in process parameters, as a random phenomenon in the laser powder bed fusion (LPBF) process, is occasional. These subtle variations can lead to defects such as porosity if not monitored and corrected promptly. Therefore, monitoring the stability of the molten pool and small changes in process parameters is significant. For process monitoring, the commonly used Transformer architectures have demonstrated remarkable performance in single-sensor signal processing. However, as the core component of the Transformer, the self-attention mechanism has limitations in data fusion of multi-sensor signals. To this end, a novel Transformer-based deep learning method associated with the cross-attention mechanism (Trans-Cross) is proposed. Trans-Cross leverages the collected photodiode and acoustic signals to monitor the fluctuation of process parameters as well as the quality of parts. Specifically, the cross-attention mechanism is introduced to enhance representative feature extraction from photodiode and acoustic signals, exploiting their complementary information. To better characterize the raw signals, an improved adaptive variational mode decomposition (VMD) algorithm is proposed for data preprocessing. The proposed Trans-Cross achieves impressive prediction accuracies of 97.41 % for identifying the fluctuations in the process parameters and 99.73 % for part quality classification. Trans-Cross exhibits superior performance and strong robustness when dealing with constraints such as limited input signal length and training data ratio. These results validate the feasibility of the proposed method in accurately identifying the fluctuations in process parameters as well as the part quality. This work provided data guidance to enhance the stability of the LPBF process.

Power-efficient full-duplex near user with power allocation and antenna selection in NOMA-based systems

This paper proposes a power scaling (PS) technique aimed at mitigating the outage floor problem commonly encountered in multi-user full-duplex (FD) non-orthogonal multiple access (NOMA) systems. Two antenna modes denominated as adaptive antenna mode (AAM) and fixed antenna mode (FAM) are utilized in this work for L FD near users in close proximity. In addition, a combined method of selecting both antenna mode and near user integrated with PS is employed to improve the overall network performance. Moreover, a power allocation between the chosen near user and far user is considered. In the low-to-moderate power regions, by AAM and FAM, we achieve twice of full diversity gain and full diversity gain, respectively. The research presents mathematical expressions for deriving the average capacity and outage probability and supports the theoretical findings with simulation-based evidence. The results of this work show that PS method not only contributes to a greater spatial diversity but also leads to superior performance compared to traditional FD NOMA systems. Moreover, our proposed method overcomes the outage floor and capacity ceiling. Furthermore, the work can be developed to 6G massive MIMO technology in multi-user FD cooperative NOMA systems.

Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods.

Microbial colonization of gypsum: from the fossil record to the present day.

Microorganisms inhabiting gypsum have been observed in environments that differ greatly in water availability. Gypsum colonized by microorganisms, including cyanobacteria, eukaryotic algae, and diverse heterotrophic communities, occurs in hot, arid or even hyperarid environments, in cold environments of the Antarctic and Arctic zones, and in saline and hypersaline lakes and ponds where gypsum precipitates. Fossilized microbial remnants preserved in gypsum were also reported. Gypsum protects the endolithic microbial communities against excessive insolation and ultraviolet radiation, while allowing photosynthetically active radiation to penetrate through the mineral substrate. We here review the worldwide occurrences of microbially colonized gypsum and the specific properties of gypsum related to its function as a substrate and habitat for microbial life on Earth and possibly beyond. Methods for detecting and characterizing endolithic communities and their biomarkers in gypsum are discussed, including microscopic, spectroscopic, chemical, and molecular biological techniques. The modes of adaptation of different microorganisms to life within gypsum crystals under different environmental conditions are described. Finally, we discuss gypsum deposits as possible targets for the search for microbial life or its remnants beyond Earth, especially on Mars, where sulfate-rich deposits occur, and propose strategies to detect them during space exploration missions.

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

Knowledge of the Stress-Health Link as a Source of Resilience Among Mexicans in the Arizona Borderlands.

Mexicans who migrate to the United States endure significant stressors related to the migration process and social and environmental conditions of life in the United States. Given that chronic stress exposure has been linked to the onset of health conditions, these ecological factors may expose them to increased risk for poor health. However, Mexicans have many positive health outcomes compared to those monitored nationally, making it crucial to understand possible sources of resilience in this population. Here, we investigate Mexicans' lay health knowledge in response to stress as a possible source of health-related resilience. Health knowledge is considered a central facet of practical and traditional knowledge as well as adaptive modes of intelligence and has a tangible impact on health. Using an ethnographically grounded community-based participatory research design informed by the theory of embodiment, our hybrid team of bilingual university and community-based researchers interviewed Mexican-origin residents (N = 30) living in rural southwestern Arizona about how they experienced and responded to stress and incorporated it into their etiological frameworks. Thematic analysis revealed that participants paid close attention to how stress presented itself in their bodies, which informed their understanding of its potentially harmful health impacts and motivated them to employ multiple stress reduction strategies. Our results highlight the breadth of Mexicans' lay health knowledge, thereby challenging dominant narratives about low rates of health literacy in this population. Findings can be harnessed to optimize potential health protective effects in home and community settings as well as to inform preventive and clinical interventions.

Effect of collaborative nursing method based on RAM model on postoperative functional reconstruction, soft tissue pain and living quality in patients with femoral trochanter fracture

ObjectiveTo explore the effect of collaborative nursing based on Roy Adaptive Mode (RAM) on postoperative functional reconstruction, soft tissue pain and quality of life in patients with femoral intertrochanteric fracture.MethodsA retrospective matched control method was used in this study. A total of 96 patients with femoral intertrochanteric fracture admitted to our hospital from July 2018 to September 2021 were selected. According to different nursing methods, the patients were divided into a collaborative group and a routine group, with 48 cases in each group. Patients in both groups were treated with intramedullary nail surgery. The routine group was given routine perioperative nursing intervention, and the collaborative group was given collaborative nursing intervention on this basis. The hip function recovery and quality of life before and after the intervention were compared between the two groups. The preoperative and postoperative pain degree, and the perioperative complications of the two groups were recorded. Logistic multivariate regression analysis was used to analyze the risk factors affecting the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation, thereby constructing a risk prediction model. ROC curve was used to analyze the clinical value of influencing factors in predicting postoperative hip function recovery in patients with femoral intertrochanteric fracture.ResultsHarris score each dimension after intervention in the collaborative group was obviously higher than that of before intervention and the conventional group (P < 0.05). After intervention, the excellent and good rate of hip joint function the collaborative group was 83.33%, which was significantly higher than 60.42% in the routine group (P < 0.05). Postoperative VAS scores each time point in the collaborative group was obviously lower than that in the routine group (P < 0.05). After intervention, the scores of physiological function, physiological role, body pain and general health in the collaborative group were significantly higher than those in the routine group (P < 0.05). The incidence of complications in the collaborative group was 6.25%, which was significantly lower than 22.92% in the routine group (P < 0.05). There were statistically significant differences in age, preoperative ASA grade, internal fixation method, osteoporosis grade and perioperative nursing methods between the excellent hip recovery group and the poor hip recovery group (P < 0.05). Logistic multivariate regression analysis showed that age, preoperative ASA grade, internal fixation method and osteoporosis grade were the risk factors affecting the recovery of hip joint function after operation, and perioperative nursing method was the protective factor (P < 0.05). Among the influencing factors, the internal fixation method and the grade of osteoporosis had certain clinical value in predicting the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation.ConclusionThe RAM model-based collaborative nursing method may effectively restore the hip joint function of patients with femoral intertrochanteric fracture after operation, and may reduce the perioperative pain degree of patients, improve the quality of life of patients and reduce the incidence of complications, which can be popularized and applied in clinical practice. In addition, there are many factors influencing the recovery of hip joint function in patients with femoral intertrochanteric fracture after operation, and targeted measures should be taken according to the influencing factors to improve the effect of intramedullary nail treatment.

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Abstract The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.&amp;#xD;

Power quality disturbance signal classification in microgrid based on kernel extreme learning machine

AbstractThis paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA–KELM achieves high classification accuracy and rapid convergence for complex PQD signals.

Adaptive cognitive tasks for mental fatigue: An innovative paradigm for cognitive loading in human performance

ObjectivesMental fatigue (MF) can impair cognitive and physical performance in sport. We tested the hypothesis that a shorter adaptive Time Load Dual Back (TLDB) task induces MF faster than a longer Stroop; and subsequently impairs cognitive and intermittent running performance. DesignThis study employed a randomized within-participant design. Methods25 trained individuals performed a Yo-Yo test after one of four experimental conditions (30-min Stroop, 20-min and 10-min TLDB tasks, and active control). Cognitive performance was assessed using the Psychomotor Vigilance Task (PVT) before and after the experimental conditions. Measures of mood, workload, MF, RPE, heart rate (HR), heart rate variability (HRV) and blood lactate were collected. ANOVAs determined the effect of the 4 conditions. ResultsStroop and 20-min TLDB conditions impaired running performance similarly (p = .015), while no differences are reported for 10-min TLDB and Control. No significant differences in physiological parameters were reported during the Yo-Yo test although RPE was significantly higher in the Stroop and 20-min TLDB conditions (p = .014). Stroop and both TLDBs conditions impaired PVT's cognitive performance (p = .029), MF (p = .012), mental demand (p < .001), HR (p = .021) and HRV (p = .033); with 20-min TLDB task having the higher significant impact. Mood alterations were similar between Stroop and TLDB conditions. ConclusionsIntermittent running (Yo-Yo) and cognitive (PVT) performances, and subjective ratings were impaired by 30-min Stroop and 20-min TLDB tasks; while 10-min TLDB did not to impair performance. Shorter adaptive modes seem to be more effective in inducing MF and could have relevant clinical applications to assess conditions such as traumatic brain injury and concussion.

Fault diagnosis of railway freight car rolling bearings based on ACMD and improved MCKD

To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions, this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition (ACMD) and an optimized Maximum Correlation Kurtosis Deconvolution (MCKD) using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation (SCSSA). Firstly, ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions (IMFs). Then, the IMFs are filtered according to the Gini coefficient indicator, with the IMF having the largest Gini coefficient selected as the optimal component. Secondly, the SCSSA is employed to iteratively optimize the filter length L, fault signal period T, and displacement parameter M in the MCKD algorithm, determining the optimal parameter combination for MCKD. This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis. The optimized MCKD is then applied to the optimal component, and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum. To verify the effectiveness and generalizability of the proposed method, simulations, experimental signals from the Case Western Reserve University Bearing Center, and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults. The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.