Search Results (47,602)

Kalman filter (KF)-based methods for 3D multi-object tracking (MOT) in autonomous driving often face challenges when detections are missed due to occlusions, sensor noise, or objects moving out of view. This leads to data association failures and cumulative errors in the update stage, as traditional Kalman filters rely on linear state estimates that can drift significantly without measurement updates. To address this issue, we propose an enhanced Kalman filter with dummy nodes and prediction confidence (KDPBTracker) to improve tracking continuity and robustness in these challenging scenarios. First, we designed dummy nodes to act as pseudo-observations generated from past and nearby frame detections in cases of missed detection, allowing for stable associations within the data association matrix when real detections were temporarily unavailable. To address the uncertainty in these dummy nodes, we then proposed a prediction confidence score to reflect their reliability in data association. Additionally, we modified a constant acceleration motion model combined with position-based heading estimation to better control high-dimensional numerical fluctuations in the covariance matrix, enhancing the robustness of the filtering process, especially in highly dynamic scenarios. We further designed bipartite graph data association to refine Kalman filter updates by integrating geometric and motion information weighted by the prediction confidence of the dummy nodes. Finally, we designed a confidence-based retention track management module to dynamically manage track continuity and deletion based on temporal and reliability thresholds, improving tracking accuracy in complex environments. Our method achieves state-of-the-art performance on the nuScenes validation set, improving AMOTA by 1.8% over the baseline CenterPoint. Evaluation on the nuScenes dataset demonstrates that KDPBTracker significantly improves tracking accuracy, reduces ID switches, and enhances overall tracking continuity under challenging conditions. Full article

The proviral load (PVL) of the bovine leukemia virus (BLV) is a useful index for estimating disease progression and transmission risk. Real-time quantitative PCR techniques are widely used for PVL quantification. We previously developed a dual-target detection method, the “Liquid Dual-CoCoMo assay”, that uses the coordination of common motif (CoCoMo) degenerate primers. This method can detect two genes simultaneously using a FAM-labeled minor groove binder (MGB) probe for the BLV long terminal repeat (LTR) region and a VIC-labeled MGB probe for the BoLA-DRA gene. In this study, we evaluated the diagnostic and analytical performance of the Dual-CoCoMo assay targeting the LTR region by comparing its performance against the commercially available Takara multiplex assay targeting the pol region. The diagnostic sensitivity and specificity of the Liquid Dual-CoCoMo assay based on the diagnostic results of the ELISA or original Single-CoCoMo qPCR were higher than those of the Takara multiplex assay. Furthermore, using a BLV molecular clone, the analytical sensitivity of our assay was higher than that of the Takara multiplex assay. Our results provide the first evidence that the diagnostic and analytical performances of the Liquid Dual-CoCoMo assay are better than those of commercially available multiplex assays that target the pol region. Full article

Background: Polyamines, including spermidine (SPD), spermine (SPM) and putrescine (PUT), are essential for cellular physiology and various cellular processes. This study aimed to examine the associations of dietary polyamines intake and all-cause mortality and incident cardiovascular disease (CVD). Methods: This prospective cohort study included 184,732 participants without CVD at baseline from the UK Biobank who had completed at least one dietary questionnaire. Diet was assessed using Oxford WebQ, a web-based 24 h recall questionnaire, with polyamines intakes estimated from previous studies. Cox proportional models with restricted cubic splines were employed to investigate nonlinear associations. The primary endpoint was all-cause mortality or incident CVD (including CVD death, coronary heart disease and stroke). Results: During a median follow-up period of 11.5 years, 7348 (3.9%) participants died and 12,316 (6.5%) developed incident CVD. Polyamines intake showed nonlinear associations with all-cause mortality and incident CVD (P for nonlinear < 0.01). Compared to the lowest quintile group of dietary polyamines intake (≤17.4 mg/day), the quintile 2 to 5 groups demonstrated a reduced risk of all-cause mortality, with the lowest risk in quintile 2 group (>17.4–22.3 mg/day) (HR:0.82, 95% CI: 0.76–0.88). Similar results were observed for incident CVD, with the lowest risk in the quintile 4 group (>27.1–33.5 mg/day) (HR: 0.86, 95% CI: 0.82–0.92). Conclusions: We found that dietary polyamines intake was associated with a lower risk of all-cause mortality or incident CVD. Furthermore, our study identified an optimal range of dietary polyamines intake. Full article

Background: Although lifestyle factors have been linked to chronic diseases among adults, their association with diagnosed individual and comorbid cardiometabolic (CMD) and pulmonary disease (PD) is not fully known. This study aimed to examine the associations between lifestyle factors and individual and comorbid CMD and PD among U.S. adults. Methods: We used cross-sectional data from the 2017–2020 National Health and Nutrition Examination Survey (n = 7394). Health care provider’s diagnosis of CMD and PD and lifestyle factors (i.e., past 5-day tobacco use, past 12-month alcohol use, diet, sleep troubles, and physical activity) were assessed. Adjusted odds ratios were estimated using logistic and multinomial logistic regression. Results: Trouble sleeping was associated with increased odds of CMD (OR: 2.47) and PD (OR: 2.29) individually, while physical activity was associated with lower odds (OR: 0.75, OR: 0.77). Past 5-day tobacco (OR: 2.36) and past year alcohol (OR: 1.61) use were associated with increased PD odds. Lifestyle factors were associated with increased odds of comorbid CMD and PD. Conclusions: Lifestyle factors were associated with increased odds of individual and comorbid CMD and PD among adults. CMD and PD prevention should involve promoting lifestyle modification and implementation of policies that eliminate structural barriers to healthy lifestyle adoption. Full article

Citizen or community science (CS) projects in the marine environment rarely consider carbon footprint and sustainability. In this case study, we assessed the effectiveness of ten CS methods used by tourists in the Great Barrier Reef Marine Park (GBRMP) and Coral Sea Marine Park (CSMP) who participated in the 2023 Citizen Science of the Great Barrier Reef expedition and the carbon footprint associated with these field methods. We also assessed the baseline coral reef knowledge of the tourists, observations of marine species, and the communication of our results to the public. Specifically, the tourists utilised up to ten methods: iNaturalist, CoralWatch, Great Barrier Reef Census, Eye on the Reef (EoR), environmental DNA (eDNA) testing kits, photogrammetry, social surveys, and Red Map, as well as marine debris and marine vegetation collections. A total of 10,421 data points were collected across 14 days, including 5390 records (52% of the total) uploaded to iNaturalist, comprising 640 plant and animal species. Public awareness of the CS expedition reached over 700,000 people based on estimates from advertising, media, social media, family and friends, and conference presentations. We estimated the total carbon footprint for the expedition as 268.7 tonnes of CO₂ or 4.47 tonnes of CO₂ per person, equivalent to AUD 112 needed to offset this input. Based on these results, our recommendations to leverage CS methods include governmental review strategies, temporal replication to allow for the measurement of changes through time, integrating sustainability into CS ecotourism platforms, and encouraging broad participation. Full article

Phase noise is a consequence of the instability inherent in the operation of oscillators, making it impossible to entirely eliminate. For low-cost internet of things (IoT) devices, this type of noise can be particularly pronounced, posing a challenge in providing high-quality localization services. To tackle this issue, this paper introduces an improved localization algorithm that includes phase noise compensation. The proposed algorithm enhances the direction of arrival (DoA) estimation for each base station by employing the EM–MUSIC method, subsequently forming a non-convex optimization problem based on the mean square error (MSE) of the estimated DoA results. Finally, a closed-form solution is derived through rational assumptions and approximations. Results show that this algorithm effectively minimizes localization errors and achieves accuracy levels within the sub-meter range. Full article

Mathematical models are designed to assist decision-making processes across various scientific fields. These models typically contain numerous parameters, the values’ estimation of which often comes under analysis when evaluating the strength of these models as management tools. Advanced artificial intelligence software has proven to be highly effective in estimating these parameters. In this research work, we use the Lotka–Volterra model to describe the dynamics of a telecommunication sector in Greece, and then we propose a methodology that employs a feed-forward neural network (NN). The NN is used to estimate the parameter’s values of the Lotka–Volterra system, which are later applied to solve the system using a fourth-algebraic-order Runge–Kutta method. The application of the proposed architecture to the specific case study reveals that the model fits well to the experiential data. Furthermore, the results of our method surpassed the other three methods used for comparison, demonstrating its higher accuracy and effectiveness. The implementation of the proposed feed-forward neural network and the fourth-algebraic-order Runge–Kutta method was accomplished using MATLAB. Full article

The thickness estimation of landslides is crucial for better landslide evaluation. Traditional non-contact mass conservation methods using 3D deformation may be unsuitable due to observation limitations. This study proposes a more feasible approach based on 2D deformation from two-track Interferometric Synthetic Aperture Radar (InSAR) observations, applied to the Xiongba landslide. The comparison with geological and drilling measurements confirms the reliability of this method. The mapped InSAR LOS deformation rate fields reveal two regions: a significantly deformed frontal zone and a relatively stable zone. Analysis suggests that surface uplift at the Xiongba-H2 landslide’s front edge results from rock–soil mass pushing in high-deformation areas. The estimated thickness ranges from 10 to 100 m, with an active volume of 6.17 × 10⁷ m³. A thicker region is identified at the front edge along the Jinsha River, posing the potential for further failure. This low-cost, easily implemented approach enhances InSAR’s applicability for landslide analysis and hazard assessment. Full article

This article addresses the developing of a framework to obtain specific GHG emissions for the railway system and proposes mitigation strategies. To achieve this purpose, a comprehensive life cycle assessment (LCA) method was employed with input data from various sources to analyze the contribution of energy consumption and the emissions of the railway system. This paper included gathering data from an infrastructure operation and maintenance for detailed GHG emissions impact. This study also presents a comparative analysis of the GHG emissions in different urban railway transportation systems in Northeast Brazil, providing valuable contextual insights. As a result of the combination of total GHG emissions analysis from the states of the Northeast Brazil railway system, a total of 11,996.11 metric tons of CO₂ equivalent (tCO₂e) was estimated. The main line traction was a prominent source of the greenhouse gas footprint, especially for the diesel traction systems at Paraiba. The proposed framework shows that significant environmental benefits can be realized with proper decision-making to increase the number of passengers–kilometer transported by rail. Full article

Background: Thyroid nodules are common yet remain a diagnostic challenge. While ultrasound and Thyroid Imaging Reporting and Data Systems (TIRADS) are accepted as standard, the use of thyroid scintigraphy in euthyroid patients is debated. The European Association of Nuclear Medicine advocates it, whereas the American Thyroid Association and European Thyroid Association do not. However, it has not been evaluated whether scintigraphy adds value to TIRADS in a multimodal approach. Our study addresses this gap by assessing the impact of integrated pertechnetate scintigraphy on TIRADS accuracy. Methods: The diagnostic performance of ACR-TIRADS, EU-TIRADS, pertechnetate scintigraphy, and multimodal models were retrospectively analyzed for 322 nodules (231 benign, 91 malignant) in 208 euthyroid patients with histopathology as a reference. Generalized estimating equations were used for statistical analysis. Results: On scintigraphy, 210 nodules were hypofunctional, 99 isofunctional, and 13 hyperfunctional. The AUC for thyroid scintigraphy, ACR-TIRADS, and EU-TIRADS were 0.6 (95% CI: 0.55–0.66), 0.83 (95% CI: 0.78–0.88), and 0.78 (95% CI: 0.72–0.83). Integrating scintigraphy with ACR-TIRADS and EU-TIRADS slightly increased diagnostic accuracy (AUC 0.86 vs. 0.83, p = 0.039 and AUC 0.80 vs. 0.78, p = 0.008) and adjusted the malignancy probability for intermediate risk TIRADS categories, with iso- or hyperfunctioning nodules in ACR-TIRADS-TR4 or EU-TIRADS-4 showing comparable malignancy probabilities as hypofunctioning nodules in TR3 or EU-TIRADS-3, respectively. Conclusions: Integrating thyroid scintigraphy with ACR- or EU-TIRADS moderately improves diagnostic performance, potentially benefiting management, especially in complex cases like multinodular goiter or indeterminate FNA. Further research is warranted to validate these findings and explore their clinical implications. Full article

In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints’ relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately. Our network consists of two stages. The first stage is composed of a two-stream network: the Real-Net stream predicts 2D image coordinates and the relative depth for each joint from the real viewpoint, while the Virtual-Net stream estimates the relative depths in virtual viewpoints for the same joints. Our network’s second stage consists of a depth-denoising module, a cropped-to-original coordinate transform (COCT) module, and a fusion module. The goal of the fusion module is to fuse skeleton information from the real and virtual viewpoints so that it can undergo feature embedding, 2D-to-3D lifting, and regression to an accurate 3D skeleton. The experimental results demonstrate that our single-view method can achieve a performance of 45.7 mm on average per-joint position error, which is superior to that achieved in several other prior studies of the same kind and is comparable to that of other sequence-based methods that accept tens of consecutive frames as the input. Full article

Background: Type 2 diabetes has become a significant global health challenge. Numerous drugs have been developed to treat the condition, either as standalone therapies or in combination when glycemic control cannot be achieved with a single medication. As existing treatments often come with limitations, there is an increasing focus on creating novel therapeutic agents that offer greater efficacy and fewer side effects to better address this widespread issue. Methods: The methylene derivatives 3a,b were coupled with phenyl/ethyl isothiocyanate in the basic medium, and dimethyl sulfate was subsequently added. Further, 5a–d were reacted with the quinoline/naphthalene hydrazides 6a,b. The target compounds 7a–g were subjected to the in vitro enzyme inhibition studies on α-glucosidase, α-amylase, and aldose reductase. Results: 7g exerted remarkable inhibitory effects on α-glycosidase [Inhibitory Concentration (IC₅₀): 20.23 ± 1.10 µg/mL] and α-amylase (17.15 ± 0.30 µg/mL), outperforming acarbose (28.12 ± 0.20 µg/mL for α-glycosidase and 25.42 ± 0.10 µg/mL for α-amylase), and exhibited a strong inhibition action on aldose reductase (12.15 ± 0.24 µg/mL), surpassing quercetin (15.45 ± 0.32 µg/mL) and the other tested compounds. In a computational study, 7g demonstrated promising binding affinities (−8.80, −8.91 kcal/mol) with α-glycosidase and α-amylase, compared to acarbose (−10.87, −10.38 kcal/mol) for α-glycosidase and α-amylase. Additionally, 7g had strong binding with aldose reductase (−9.20 kcal/mol) in comparison to quercetin (−9.95 kcal/mol). Molecular dynamics (MDs) simulations demonstrated that 7g remained stable over a 100 ns simulation period, and the binding free energy estimates remained consistent throughout this time. Conclusions: We reported the modification of quinoline and naphthalene rings to hydrazineylidene–propenamides 7a–g using various synthetic approaches. 7g emerged as a leading candidate, exhibiting greater inhibition of α-glycosidase, α-amylase, and aldose reductase. These findings underscore their potential as essential molecules for the development of innovative antidiabetic treatments. Full article

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article

To achieve economic resilience and green, low-carbon development are two goals of China’s high-quality economic development. This paper uses the entropy weight method and coupling coordination degree model to estimate the coupling coordination level of economic resilience and green, low-carbon development. Kernel density estimation, Moran index, Dagum Gini coefficient, Markov chain, and obstacle degree model are used to explore the spatiotemporal evolution characteristics and obstacle factors. The results are as follows. (1) The coupling coordination degree between China’s economic resilience and green, low-carbon development has increased overall. However, the eastern region has the highest, and the central region has the fastest growth. (2) The coupling coordination degree shows positive spatial autocorrelation, with most provinces exhibiting high–high or low–low aggregation characteristics. (3) The contribution of imbalance mainly comes from inter-regional differences, but the contribution of intra-regional differences to imbalance is increasing. (4) The spatio-temporal evolution pattern is generally better, and the probability of the coupling coordination degree maintaining the initial state is the largest. The neighborhood’s state affects the transition probability but does not affect that of high-level provinces. (5) Innovation capacity is the main obstacle to improving economic resilience, and per capita water resources are the main obstacle to green and low-carbon development. Finally, this paper puts forward suggestions for creating a good innovation environment, increasing R&D investment, promoting green technology progress, optimizing regional cooperation and resource allocation, and promoting industrial green transformation. Full article

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (${\sigma }_{vv}^0$), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage. Full article