Search Results (6,557)

Background: Positron emission tomography (PET) provides physiologic information central to oncologic staging and treatment assessment, but its availability is limited by cost, radiation exposure, and scanner access. Synthesizing PET from computed tomography (CT) is attractive but challenging, as tracer uptake is only partially constrained by anatomy, making the mapping inherently one-to-many. Methods: We propose a conditional 3D latent diffusion framework (3D-LDM) for CT-to-PET synthesis in the head–neck and thoracic region. The pipeline localizes anatomy by segmenting lungs in CT and restricting the volume to reduce irrelevant variability. PET volumes are encoded into a compact latent space using a KL-regularized 3D autoencoder, and a conditional 3D diffusion U-Net learns to generate PET latents conditioned on CT via a denoising diffusion process. The model was trained and evaluated on 900 paired PET/CT studies. Performance was assessed in SUV space using MAE, PSNR, and SSIM, and compared against transformer-, CNN-, and GAN-based baselines. Results: On the held-out test cohort, 3D-LDM achieved the best overall quantitative fidelity (MAE = 303.05 ± 22.16 SUV units, PSNR = 32.64 ± 1.79, SSIM = 0.86 ± 0.03), outperforming all baselines with statistically significant differences (p < 0.001). At the lesion level, the model achieved a precision of 0.76 (95% CI: 0.71, 0.81) and recall of 0.76 (95% CI: 0.72, 0.80), detecting an average of 3.19 lesions per scan with a false-positive rate of 0.72/scan. Lesion-wise NMSE was 11.37%, significantly outperforming GAN and transformer baselines. Conclusions: 3D-LDM enables efficient, high-fidelity PET synthesis in the head–neck and thoracic regions, substantially improving lesion-level accuracy over state-of-the-art baselines. While it is not a replacement for diagnostic PET, these results support the model’s potential as a clinical decision support tool. Full article

Achieving the coexistence of high energy density and fast-charging capability remains a fundamental challenge for lithium-ion batteries. Increasing electrode thickness and compaction density enhances energy density but simultaneously alters the pore structure and restricts lithium-ion transport, leading to concentration polarization, increased resistance, and lithium plating. In this work, we employ X-ray computed tomography (X-CT) and 3D reconstruction to establish quantitative relationships between particle size, compaction density, and key structural parameters (porosity, tortuosity, effective proportion of lithium-ion flux (f_eff)). Then, an electrochemical model is used to link the liquid-phase kinetic parameters (ionic conductivity (k₀) and liquid-phase diffusion coefficient), as corrected by the effective proportion of lithium-ion flux f_eff, to polarization and lithium-plating behavior, and the maximum current density without lithium plating under various fabrication conditions is finally determined. Results show that small-particle electrodes exhibit superior rate capability at moderate compaction levels, but suffer from rapidly increasing tortuosity and reduced transport efficiency under high compaction and large thickness. Moreover, a double-layer gradient electrode design effectively integrates the advantages of both large- and small-particle architectures, enabling high-rate operation without lithium plating. The double-layer gradient electrode (ρ = 1.6 g/cm³) exhibited ~50% higher performance at 1.5 C compared to the small-particle anode and enabled 2 C charging without lithium plating. This study offers a robust structural design strategy for optimizing thick-electrode architectures toward high-energy, fast-charging LIBs. Full article

The score set of a tournament is defined as the set of its distinct out-degrees. In 1978, Reid proposed the conjecture that for any set of nonnegative integers D, there exists a tournament T with a score set D. In 1989, Yao presented an arithmetic proof of the conjecture, but a general polynomial-time construction algorithm has not been discovered. This paper proposes a necessary and sufficient condition and a separate necessary condition, based on the existing Landau’s theorem for the problem of reconstructing score sequences from score sets of tournament graphs. The necessary condition introduces a structured set that enables the use of group-theoretic techniques, offering not only a framework for solving the reconstruction problem but also a new perspective for approaching similar problems. In particular, the same theoretical approach can be extended to reconstruct valid score sets given constraints on the frequency of distinct scores in tournaments. Based on these conditions, we have developed three algorithms that demonstrate the practical utility of our framework: a polynomial-time algorithm and a scalable algorithm for reconstructing score sequences, and a polynomial-time network-building method that finds all possible score sequences for a given score set. Moreover, the polynomial-time algorithm for reconstructing the score sequence of a tournament for a given score set can be used to verify Reid’s conjecture. These algorithms have practical applications in sports analysis, ranking prediction, and machine learning tasks such as learning-to-rank models and data imputation, where the reconstruction of partial rankings or sequences is essential for recommendation systems and anomaly detection. Full article

The rapid deployment of 5G networks and the proliferation of Internet of Things (IoT) devices have significantly increased the complexity of urban electromagnetic radiation (EMR) environments. Conventional ground-based monitoring systems are spatially limited and unable to provide three-dimensional field characterization. This paper proposes an integrated IoT–UAV framework for high-resolution EMR monitoring, spatial reconstruction, and intelligent source classification. A four-layer architecture combining distributed sensing, edge computing, cloud analytics, and visualization is developed. A formal electromagnetic propagation model is introduced to ensure consistency between broadband exposure measurements and frequency-selective spectral analysis. A CNN–LSTM architecture is implemented for spectral–temporal source classification, achieving 95% validation accuracy across five EMR categories. Simulation-based validation demonstrates up to an eightfold improvement in spatial coverage compared to fixed ground networks while maintaining a practical anomaly detection threshold of −55 dBm in the spectrum-analysis RF chain. The proposed framework establishes a mathematically consistent and practically deployable solution for next-generation EMR monitoring systems. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

Quantifying anthropogenic CO₂ increments is vital for assessing emission reductions. Using a seamless XCO₂ dataset over China reconstructed from OCO-2/3 satellite retrievals and machine learning, combined with EOF decomposition and LISA analysis, this study investigates XCO₂ anomalies and local anthropogenic increments (dXCO₂) at national and urban agglomeration scales. Nationally, XCO₂ anomalies exhibit a “southeast positive, northwest negative” spatial pattern aligning with human activities and a “winter high, summer low” seasonal cycle. EOF analysis reveals four dominant modes: anthropogenic–natural trade-offs, East Asian summer monsoon modulation, local emissions, and baseline context. At the regional scale, multi-year mean dXCO₂ (2015–2019) in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are 3.46 ± 0.45 ppm, 1.30 ± 0.36 ppm, and 0.08 ± 0.14 ppm, respectively, showing higher values in northern heavy industrial zones. During the 2020–2022 pandemic, dXCO₂ decreased in BTH (2.28 ± 0.73 ppm) and YRD (1.16 ± 0.43 ppm) but increased in PRD (0.28 ± 0.27 ppm). Compared to pre-pandemic levels, lockdowns saw dXCO₂ decrease slightly in YRD while increasing in BTH and PRD, reflecting differential responses of regional industrial structures. This study demonstrates the potential of seamless XCO₂ data for monitoring anthropogenic enhancement signals, and the proposed LISA-based method offers new support for regionally differentiated emission reduction assessments. Full article

Background: Endoscopic sacrocolpopexy (ESC) is a widely performed procedure for pelvic organ prolapse, with laparoscopic sacrocolpopexy (LSC) and robotic-assisted sacrocolpopexy (RSC) approaches. However, suturing to the anterior longitudinal ligament at the sacral promontory carries a risk of massive hemorrhage due to presacral vascular injury. This study aimed to determine the frequency of presacral venous variations considered clinically relevant during suturing at the promontory and to explore their association with perioperative outcomes using contrast-enhanced three-dimensional computed tomography (3D-CT). Methods: Among 319 consecutive ESC cases performed between 2014 and 2025, 265 patients who underwent preoperative contrast-enhanced CT were retrospectively analyzed in this single-center cohort study. Two vascular findings were defined as clinically significant: (1) anomalous drainage of the internal iliac vein into the contralateral common iliac vein and (2) a clearly visualized median sacral vein on 3D reconstruction. The clinical impact of vascular abnormalities was evaluated using surgical time, blood loss, and perioperative complication rates as indicators. Student’s t-test was used for comparing continuous variables, and the chi-squared test was used for comparing categorical variables. The data for this study were retrospectively collected from electronic medical records, anonymized, and then analyzed. Results: Anomalous internal iliac vein drainage was observed in 11.3% (30/265), and a visible median sacral vein was observed in 10.2% (27/265). Overall, 17.7% (47/265, CI: 13.2–22.2%) of patients had at least one clinically significant variation. There were no significant differences between the groups in terms of age, parity, BMI, operative time, blood loss, or perioperative complication rates. No cases required transfusion. Conclusions: Clinically significant presacral vein mutations were present in approximately 1 in 6 patients. The main findings of this study are that clinically significant presacral vascular mutations are relatively frequent (17.7%) in ESC and that there was no significant difference in perioperative outcomes between patients with and without vascular mutations. Clinically relevant presacral vascular variations are relatively common in ESC. Preoperative contrast-enhanced 3D-CT may support risk assessment and surgical planning. Full article

The Yellow Sea is an important offshore area in China, and the accurate prediction of its seawater temperature is of great significance for marine environmental monitoring and climate adaptation management. However, existing research on predicting the three-dimensional (3D) temperature field in the Yellow Sea is scarce and insufficiently accurate. This study proposes a TCN-U-Net++ fusion model to reconstruct the Yellow Sea temperature field using remote sensing satellite data and SODA reanalysis data, while considering the influence of a series of factors, including wind (USSW and VSSW), absolute bathymetric data (BAT), sea surface height anomaly (SSHA), latitude (LAT), longitude (LON), solar radiation (SR), surface runoff (SRO), and precipitation (P). The results show that the model can accurately capture the temporal and spatial distribution characteristics of the temperature field in the Yellow Sea. The results indicate that the deviations from SODA are generally within 2 °C, with errors being approximately 45% lower than those of other models, while the prediction errors relative to Argo and voyage observations are mostly within 1 °C, further demonstrating the accuracy and robustness of the proposed model. In addition, the predictions of the Yellow Sea Cold Water Mass (CWM) are highly consistent with SODA in terms of their evolution and key characteristic parameters. Specifically, the maximum deviation in core temperature is only 0.3 °C, and the difference in its spatial extent is less than 1%. The results demonstrate that TCN-U-Net++ effectively enhances the accuracy of 3D sea temperature prediction in the Yellow Sea, providing technical support for temperature monitoring, ecological early warning, and climate change research. Full article

Geomembranes are extensively used for seepage control in the reservoir of pumped-storage power stations due to their superior deformability, ease of construction, and low cost. The deformation behavior of geomembranes under high hydraulic pressure is of great importance for seepage-control design and operational safety evaluation. Nevertheless, existing hydrostatic pressure resistance tests cannot effectively measure the hydraulic bulging deformation of geomembranes subjected to water pressure. This study proposes a non-contact binocular vision method to quantify the hydraulic bulging deformation of geomembranes. The method combines underwater camera calibration, image enhancement, stereo matching, triangulation, and three-dimensional reconstruction to achieve both visualization and accurate measurement of geomembrane deformation. After experimental validation and accuracy calibration, the proposed method was preliminary applied to four geomembrane materials, including HDPE, LLDPE, PVC, and TPO, under hydraulic loading. The results show that the measurement error is less than 5% in the large-deformation range under medium and high water pressures. The method can effectively capture the hydraulic bulging behavior of geomembranes and accurately characterize the deformation features of different materials under high hydraulic pressure. This study provides a practical technical approach for underwater deformation measurement of geomembranes and supports seepage-control design and operational safety monitoring. Full article

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces. Full article

Underwater image enhancement has been widely studied to improve visual quality; however, its impact on downstream geometric tasks such as sparse 3D reconstruction remains insufficiently understood. In particular, visually enhanced images do not necessarily lead to improved feature matching or reconstruction performance. This work addresses the problem of selecting appropriate preprocessing strategies for underwater Structure-from-Motion (SfM) pipelines from a task-oriented perspective. We propose a lightweight machine-learning-based preprocessing selector that predicts reconstruction performance from image statistics and recommends suitable enhancement strategies for each input sequence. To ensure reliable evaluation, we introduce a grouped leave-one-parent-sequence-out protocol that avoids overlap-induced bias common in clip-wise splitting. Experiments are conducted on challenging underwater datasets derived from the Real-world Underwater Image Enhancement (RUIE) benchmark, with the primary comparison variable defined as the number of reconstructed sparse 3D points. Supporting geometric variables, including the number of registered images, mean track length, and mean reprojection error, are recorded for interpretation. Results show that preprocessing choices significantly affect reconstruction outcomes and that the optimal strategy is scene-dependent. The proposed selector consistently improved over raw input on the evaluated grouped subset and remained competitive with a strong fixed preprocessing baseline. The grouped leave-one-parent-sequence-out protocol is intended to reduce overlap-induced bias common in clip-wise splitting and to provide a more conservative estimate of generalization. This work highlights the importance of task-aware preprocessing and reliable evaluation in underwater vision systems, offering practical insights for deploying enhancement strategies in real-world 3D reconstruction pipelines. Full article

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

The study of veterinary anatomy is gradually progressing with the combination of digital imaging and artificial intelligence (AI). This paper aimed to evaluate the potential use of AI tools for morphometric analysis and the anatomical identification of the ovine brain. Five adult specimens were used and approached through traditional dissection and fixation methods followed by digital photography and manual measurement with high-precision Vernier calipers. These results were compared against AI-based approaches, including DeeVid AI for 3D reconstruction, Imageonline for digital measurement, and ChatGPT/Artlist for anatomical nomenclature. The findings indicate that AI tools like DeeVid AI significantly enhance structural visualization, and Imageonline provides high-precision measurements comparable to manual tools (p > 0.05). However, AI-driven anatomical naming remains prone to significant errors, with ChatGPT and Artlist exhibiting error rates of 87.5% and 70.8%, respectively, in specific neuroanatomical labeling. This study concludes that while AI eases the reshaping and measurement of anatomical structures, human expertise remains indispensable for accurate anatomical identification. Full article

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

Near-infrared hyperspectral imaging (NIR-HSI) is widely used as a non-destructive technique for evaluating internal fruit quality; however, reliable pixel-wise visualization remains challenging due to geometry-induced spectral distortions and the lack of statistically interpretable validation criteria. This study proposes an integrated framework for three-dimensional visualization of soluble solids content (SSC) across the entire surface of strawberries using NIR-HSI combined with shape-aware spectral correction and pixel-level reliability assessment. Two complementary imaging systems—a line-scan system and a rotation-scan system—were used to acquire hyperspectral and 3D shape data. Fruit height and surface orientation were incorporated into spectral preprocessing to reduce illumination and curvature effects. Partial least squares regression (PLSR) models were developed using region-of-interest-averaged spectra and applied to pixel-wise SSC mapping. To assess the statistical validity of pixel-level predictions, an imaging reliability index based on the Mahalanobis distance in the PLS score space was introduced. The results show that models with high sample-level accuracy do not necessarily produce reliable SSC maps, whereas reliability-based model selection improves image interpretability. This framework enables consistent three-dimensional SSC visualization and is applicable to hyperspectral imaging of internal fruit attributes. Full article