Dissecting molecular mechanisms underlying the inhibition of ß-glucuronidase by alkaloids from Hibiscus trionum: Integrating in vitro and in silico perspectives.

ß-Glucuronidase, a crucial enzyme in drug metabolism and detoxification, represents a promising target for therapeutic intervention due to its potential to modulate drug pharmacokinetics and enhance therapeutic efficacy. Herein, we assessed the inhibitory potential of phytochemicals from Hibiscus trionum against ß-glucuronidase. Grossamide and grossamide K emerged as the most potent ß-glucuronidase inhibitors with IC50 values of 0.73 ± 0.03 and 1.24 ± 0.03 µM, respectively. The investigated alkaloids effectively inhibited ß-glucuronidase-catalyzed PNPG hydrolysis through a noncompetitive inhibition mode, whereas steppogenin displayed a mixed inhibition mechanism. Molecular docking analyses highlighted grossamide and grossamide K as inhibitors with the lowest binding free energy, all compounds successfully docked into the same main binding site occupied by the reference drug Epigallocatechin gallate (EGCG). We explored the interaction dynamics of isolated compounds with ß-glucuronidase through a 200 ns molecular dynamics (MD) simulation. Analysis of various MD parameters revealed that grossamide and grossamide K maintained stable trajectories and demonstrated significant energy stabilization upon binding to ß-glucuronidase. Additionally, these compounds exhibited the lowest average interaction energies with the target enzyme. The MM/PBSA calculations further supported these findings, showing the lowest binding free energies for grossamide and grossamide K. These computational results are consistent with experimental data, suggesting that grossamide and grossamide K could be potent inhibitors of ß-glucuronidase.

Enhancing interfacial electron transfer through PANI electron bridge for tailoring dynamic reconstruction and achieving high-performance water oxidation.

Tailoring the dynamic reconstruction of transition metal compounds into highly active oxyhydroxides through surface electron state modification is crucial for advancing water oxidation, yet remains a formidable challenge. In this study, a unique polyaniline (PANI) electron bridge was integrated into the metal-organic frameworks (MOFs)/layer double hydroxides (LDHs) heterojunction to expedite electron transfer from MOFs to LDHs, facilitating electron accumulation at the metal sites within MOF and electron-deficient LDHs. This configuration promotes the surface dynamic reconstruction of LDHs into highly active oxyhydroxides while safeguarding the MOF from corrosion in harsh environments over extended periods. The optimized electronic structure modification of both MOFs and LDHs enhances reaction kinetics. The superior MIL-88B(Fe)@PANI@NiCo LDH catalyst achieved 10 mA∙cm-2 at an overpotential of 202 mV and demonstrated stable operation for 120 h at this current density. This research introduces an innovative approach for guiding electron transfer and dynamic catalyst reconstruction by constructing a PANI electron bridge, potentially paving the way for more efficient catalytic systems.

Intrinsic dynamics of emulsions: Experiments in microgravity on the International Space Station.

HYPOTHESIS: In order to understand the basic mechanisms affecting emulsion stability, the intrinsic dynamics of the drop population must be investigated. We hypothesize that transient ballistic motion can serve as a marker of interactions between drops. In 1G conditions, buoyancy-induced drop motion obscures these interactions. The microgravity condition onboard the International Space Station enable this investigation. EXPERIMENTS: We performed Diffusing Wave Spectroscopy (DWS) experiments in the ESA Soft Matter Dynamics (SMD) facility. We used Monte Carlo simulations of photon trajectory to support data analysis. The analysis framework was validated by ground-based characterizations of the initial drop size distribution (DSD) and the properties of the oil/water interface in the presence of surfactant. FINDINGS: We characterized the drop size distribution and found to be bi-disperse. Drop dynamics shows transient ballistic features at early times, reaching a stationary regime of primarily diffusion-dominated motion. This suggests different ageing mechanisms: immediately after emulsification, the main mechanism is coalescence or aggregation between small drops. However at later times, ageing proceeds via coalescence or aggregation of small with large drops in some emulsions. Our results elucidate new processes relevant to emulsion stability with potential impact on industrial processes on Earth, as well as enabling technologies for space exploration.

Optimal synchronization with L2-gain performance: An adaptive dynamic programming approach.

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton-Jacobi-Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton-Jacobi-Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton-Jacobi-Isaacs equation. Second, the L2-gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.

Analysis of dynamic evolution characteristics and driving factors of regional synergistic effect of reducing carbon and saving water: the case of Yangtze River Delta Urban Agglomerations, China.

Carbon emissions and water consumption are both important factors affecting sustainable development. Therefore, it is necessary to put them in the same research framework and investigate the synergy. In this study, the dynamic evolution characteristics of the synergistic effect of reducing carbon and saving water (RCSW) were analyzed. Then, taking the Yangtze River Delta Urban Agglomerations (YRDUA) as the research object, the influencing factors and specific paths of the synergistic effect were clarified. The results showed that the low-carbon emission efficiency (LCEE) had a stable synergy with the intensive utilization efficiency of water resources (IUEWR) in the YRDUA. Government financial expenditure, actual use of foreign capital, and population density were the most significant driving forces for the synergistic effect of RCSW, with q values of 0.561, 0.363, and 0.240, respectively. In addition, most of the interactions of the driving factors were nonlinear enhancement and double-factor enhancement.

Dynamics of direct hydrocarbon PEM fuel cells.

Hydrocarbon fuels contain approximately 50 times more energy per unit mass than commercial batteries, thus converting even 10% of the energy contained in hydrocarbon fuels to electrical energy could present a more mass-efficient electrical energy source than batteries. Considering the storability of hydrocarbon fuels compared to hydrogen, the viability of direct hydrocarbon polymer electrolyte membrane fuel cells was examined. With extremely pure (> 99.99%) propane, the cell Open-Circuit Voltage (OCV) was only 0.05 V and produced negligible power. However, with addition of trace quantities of unsaturated hydrocarbons, the cell had an OCV of 0.85 V and produced power, even after the unsaturated hydrocarbon addition was discontinued. At sufficiently high current densities, power output gradually decreased then the cell rapidly "extinguished" but by periodically shutting off the current for short time intervals the average power density could be increased significantly. Chemical analysis revealed that no significant amounts of hydrocarbon intermediates or CO were present in the effluent and that conversion of the hydrocarbon fuel to CO2 and H2O was nearly complete. An analytical model incorporating the relative rates of conversion of active anode catalyst sites to inactive sites and vice versa was developed to interpret this behavior. The model predictions were consistent with the experimental observations; possible physical mechanisms are discussed.

Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region.

OBJECTIVES: To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). METHODS: A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). RESULTS: The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. CONCLUSION: Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. CLINICAL RELEVANCE STATEMENT: The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. KEY POINTS: We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI.

Relationship between 1,5-anhydroglucitol and renal function assessed by dynamic renal scintigraphy in type 2 diabetes.

CONTEXT: The reliability of serum 1,5-anhydroglucitol (1,5-AG) in type 2 diabetic patients with renal insufficiency remains controversial. OBJECTIVE: To evaluate the relationship between renal function and serum 1,5-AG, and to assess the extent to which renal function influences 1,5-AG. METHODS: A total of 5337 participants with type 2 diabetes were enrolled. The measured glomerular filtration rate (mGFR) was assayed using 99mTc-DTPA dynamic renal scintigraphy. All subjects were stratified into five groups based on mGFR (≥ 120 [n = 507], 90-120 [n = 2015], 60-90 [n = 2178], 30-60 [n = 604], and < 30 mL/min/1.73 m2 [n = 33]). RESULTS: Overall, the serum 1,5-AG and mGFR levels were 3.3 (1.7-7.0) µg/mL and 88.6 ± 24.1 mL/min/1.73 m2, respectively. mGFR was found to be negatively correlated with 1,5-AG levels (r = -0.189, P < 0.001). Multiple linear regression revealed that mGFR was independently and negatively related to serum 1,5-AG after adjusting for covariates including HbA1c (P < 0.001). In subgroups with mGFR ≥ 30 mL/min/1.73 m2, the correlation coefficients between 1,5-AG and HbA1c, fasting plasma glucose, postprandial plasma glucose, and the differences between postprandial and fasting plasma glucose remained significant (range from -0.126 to -0.743, all P < 0.01). However, the link between 1,5-AG and traditional glycemic markers was attenuated in individuals with mGFR < 30 mL/min/1.73 m2. Sensitivity analysis after excluding anemic patients showed similar results regarding the relationship between serum 1,5-AG and HbA1c across the mGFR subgroups. CONCLUSIONS: Although we observed a weak inverse correlation (r = -0.189) between mGFR and serum 1,5-AG in type 2 diabetes, 1,5-AG remains a valid marker for assessing glucose control in subjects with mild or moderate renal dysfunction.

Comparison of golden-angle radial sparse parallel (GRASP) and conventional cartesian sampling in 3D dynamic contrast-enhanced mri for bladder cancer: a preliminary study.

PURPOSE: To compare the image quality, inter-reader agreement, and diagnostic capability for muscle-invasive bladder cancer (MIBC) of the reconstructed images in sections orthogonal to the bladder tumor obtained by 3D Dynamic contrast-enhanced (DCE)-MRI using the Golden-angle Radial Sparse Parallel (GRASP) technique with the images directly captured using the Cartesian sampling. MATERIALS AND METHODS: This study involved 68 initial cases of bladder cancer examined with DCE-MRI (GRASP: n = 34, Cartesian: n = 34) at 3 Tesla. Four radiologists conducted qualitative evaluations (overall image quality, absence of motion artifact, absence of streak artifact, and tumor conspicuity) using a five-point Likert scale (5 = Excellent/None) and quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. The areas under the receiver-operating characteristic curves (AUCs) for the Vesical Imaging-Reporting and Data System (VI-RADS) DCE score for MIBC assessment were calculated. Inter-reader agreement was also assessed. RESULTS: GRASP notably enhanced overall image quality (pooled score: GRASP 4 vs. Cartesian 3, P < 0.0001), tumor conspicuity (5 vs. 3, P < 0.05), SNR (Median 38.2 vs. 19.0, P < 0.0001), and CNR (7.9 vs. 6.0, P = 0.005), with fewer motion artifacts (5 vs. 3, P < 0.0001) and minor streak artifacts (5 vs. 5, P > 0.05). Although no significant differences were observed, the GRASP group tended to have higher AUCs for MIBC (pooled AUCs: 0.92 vs. 0.88) and showed a trend toward higher inter-reader agreement (pooled kappa-value: 0.70 vs. 0.63) compared to the Cartesian group. CONCLUSIONS: Using the GRASP for 3D DCE-MRI, the reconstructed images in sections orthogonal to the bladder tumor achieved higher image quality and improve the clinical work flow, compared to the images directly captured using the Cartesian. GRASP tended to have higher diagnostic ability for MIBC and showed a trend toward higher inter-reader agreement compared to the Cartesian.

Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function.

Objective: White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective. Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system. Results: The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch." Conclusion: Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education.

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series.

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

A user-friendly method for estimating discrete choice land-use model in a panel data setting.

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Upper blepharoplasty in Asian population: A novel technique based on functional zoning and dynamic reconstruction with anatomical structure.

BACKGROUND: Double eyelid blepharoplasty is the most popular cosmetic operation in Asian population. Various surgical techniques have been developed in order to create dynamic eyelid folds with natural-looking in recent years, but postoperative complications like so-called sausage-like appearance have not been resolved completely. AIMS: To create natural-looking dynamic folds, we propose a new method imitating the original anatomical structure of congenital double-eyelid. PATIENTS AND METHODS: Eighty-six patients who underwent the double-eyelid surgery from June 1st, 2018 to October 31st, 2020 were included in this retrospective study, including 10 males and 76 females, aged 18-39 years (mean 27.4 ± 5.6 years). All the included patients received double eyelid surgery performed by the same senior doctor, using the pretarsal orbicularis oculi muscle-releasing technique. Patient Reported Outcome Measures questionnaires were administrated to assess the severity of scarring, pain, and asymmetry, as well as functional and appearance issues. Surgical outcome was assessed through objective and subjective evaluation forms (PROM and patient satisfaction rate). RESULTS: Among the 86 patients, 5 were lost during the follow-up period. The absolute number of enrolled patients is 81. 91.36% of the enrolled patients reported minimal or non-visible scarring at the double eyelid incision. As to functional and appearance issues, the main problem were asymmetry (12.35%) and the narrowing of the supratarsal crease width (8.64%). No supratarsal depression and "sausage-like" appearance occurred in this study. 95.1% of patients reported either good or excellent outcome (mean score: 108 of 120) based on analysis of PROM results, and 96.3% of patients reported either high or very high satisfaction (mean score: 96 of 120) for the patient satisfaction assessment. CONCLUSIONS: This new surgical method of double-eyelid blepharoplasty provides comparatively safe and effective results.

A Dynamic Cellular Model as an Emerging Platform to Reproduce the Complexity of Human Vascular Calcification In Vitro.

Vascular calcification (VC) is a cardiovascular disease characterized by calcium salt deposition in vascular smooth muscle cells (VSMCs). Standard in vitro models used in VC investigations are based on VSMC monocultures under static conditions. Although these platforms are easy to use, the absence of interactions between different cell types and dynamic conditions makes these models insufficient to study key aspects of vascular pathophysiology. The present study aimed to develop a dynamic endothelial cell-VSMC co-culture that better mimics the in vivo vascular microenvironment. A double-flow bioreactor supported cellular interactions and reproduced the blood flow dynamic. VSMC calcification was stimulated with a DMEM high glucose calcification medium supplemented with 1.9 mM NaH2PO4/Na2HPO4 (1:1) for 7 days. Calcification, cell viability, inflammatory mediators, and molecular markers (SIRT-1, TGFß1) related to VSMC differentiation were evaluated. Our dynamic model was able to reproduce VSMC calcification and inflammation and evidenced differences in the modulation of effectors involved in the VSMC calcified phenotype compared with standard monocultures, highlighting the importance of the microenvironment in controlling cell behavior. Hence, our platform represents an advanced system to investigate the pathophysiologic mechanisms underlying VC, providing information not available with the standard cell monoculture.

Real-World Spatial Synchronization of Event-CMOS Cameras through Deep Learning: A Novel CNN-DGCNN Approach.

This paper presents a new deep-learning architecture designed to enhance the spatial synchronization between CMOS and event cameras by harnessing their complementary characteristics. While CMOS cameras produce high-quality imagery, they struggle in rapidly changing environments-a limitation that event cameras overcome due to their superior temporal resolution and motion clarity. However, effective integration of these two technologies relies on achieving precise spatial alignment, a challenge unaddressed by current algorithms. Our architecture leverages a dynamic graph convolutional neural network (DGCNN) to process event data directly, improving synchronization accuracy. We found that synchronization precision strongly correlates with the spatial concentration and density of events, with denser distributions yielding better alignment results. Our empirical results demonstrate that areas with denser event clusters enhance calibration accuracy, with calibration errors increasing in more uniformly distributed event scenarios. This research pioneers scene-based synchronization between CMOS and event cameras, paving the way for advancements in mixed-modality visual systems. The implications are significant for applications requiring detailed visual and temporal information, setting new directions for the future of visual perception technologies.

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer.

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

Multi-Armed Bandit-Based User Network Node Selection.

In the scenario of an integrated space-air-ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node's network state conforms to a normal distribution, and by constructing the distribution's expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput.

A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools.

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages.

Improved Video-Based Point Cloud Compression via Segmentation.

A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points' proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.