Search Results (512)

Electron transport in carbon nanotubes (CNTs) is highly sensitive to interactions with their local environment, making them promising candidates for sensing applications. Specifically, this could allow detection of electrochemically and optically inert compounds that typically require complex and expensive analytical techniques. In this study, we examine how single-walled carbon nanotubes (SWCNTs) respond to perfluorooctanoic acid (PFOA), a common per- and polyfluoroalkyl substance (PFAS). To improve sensitivity, we employ a single/few-CNT device setup where a small number of SWCNTs were aligned across nanogaps between gold electrodes with the dielectrophoresis method. This structure addresses the challenges of large CNT networks, such as inter-CNT interactions, drift, and degradation, resulting in improved stability for practical applications. Results showed that device resistance drops as a function of PFOA concentrations. Additionally, positive gate voltage enhances sensitivity by attracting negatively charged PFOA molecules to the SWCNT surface. Specifically, we report that the sensitivity increases by nearly an order of magnitude under a 0.3 V gate bias. Impedance spectroscopy reveals distinct amplitude and phase signatures, enabling selective detection of PFOA among different analytes. Applying gate voltage further enhances sensor selectivity, highlighting the potential of gated SWCNT devices for accurate and selective environmental monitoring. The device demonstrates promising performance as a robust platform for creating single/few-CNT nanosensors for detecting electrochemically and optically inert substances like PFAS molecules. Full article

Real-time, reliable forecasting of water quality (WQ) is a critical component of sustainable environmental management. A key challenge, however, is modeling time-varying uncertainty (heteroscedasticity), particularly during disruptive events like storms where predictability decreases dramatically. Standard probabilistic models often fail in these high-stakes scenarios, producing forecasts that are either too conservative during calm periods or dangerously overconfident during volatile events. This paper introduces the Gated Hybrid–Mixture Density Network (GH-MDN), an architecture explicitly designed for adaptive uncertainty estimation. Its core innovation is a dedicated gating network that learns to adaptively modulate the prediction interval width in response to a domain-relevant, event-precursor signal. We evaluate the GH-MDN on both synthetic and real-world WQ datasets using a rigorous cross-validation protocol. The results demonstrate that our gated model provides robust calibration and trustworthy adaptive coverage; specifically, it successfully widens prediction intervals to capture extreme events where standard benchmarks fail. We further show that common aggregate metrics such as CRPS can mask over-confident behavior during rare events, underscoring the need for evaluation approaches that prioritize calibration. This science-informed approach to modeling heteroscedasticity prioritizes reliable risk coverage over aggregate error minimization, marking a critical step towards the development of more trustworthy environmental forecasting systems. Full article

Time-of-flight (ToF) image sensors must operate across a wide span of reflected-light intensities, from weak diffuse reflections to extremely strong retroreflections. We present a signal-intensity range-extension technique that linearizes the linear–logarithmic (Lin–Log) pixel response for short-pulse multi-tap indirect ToF (iToF) sensors. Per-pixel two-region (2R) and three-region (3R) models covering the linear, transition, and logarithmic regimes are derived and used to recover a near-linear signal. Compared with a two-region approach that does not linearize the transition region, the 3R method substantially improves linearity near the knee point if extremely high linearity is required. Experiments with a six-tap iToF imager validate the approach. Depth imaging shows that linearization with common parameters reduces average error but leaves pixel-wise deviations, whereas pixel-wise 3R linearization yields accurate and stable results. Range measurements with a retroreflective target moved from 1.8–13.0 m in 0.20 m steps and achieved centimeter-level resolution and reduced the linearity-error bound from ±6.7%FS to ±1.5%FS. Residual periodic deviations are attributed to small pulse-width mismatches between the illumination and demodulation gates. These results demonstrate that Lin–Log pixels, combined with pixel-wise three-region linearization, enable robust ToF sensing over an extended dynamic range suitable for practical environments with large reflectance variations. Full article

Deep learning methods based on convolutional neural networks (CNNs) and Mamba have advanced medical image segmentation, yet two challenges remain: (1) trade-off in feature extraction, where CNNs capture local details but miss global context, and Mamba captures global dependencies but overlooks fine structures, and (2) limited feature aggregation, as existing methods insufficiently integrate inter-layer common information and delta details, hindering robustness to subtle structures. To address these issues, we propose a hybrid cascade and dual-path adaptive aggregation network (HCDAA-Net). For feature extraction, we design a hybrid cascade structure (HCS) that alternately applies ResNet and Mamba modules, achieving a spatial balance between local detail preservation and global semantic modeling. We further employ a general channel-crossing attention mechanism to enhance feature expression, complementing this spatial modeling and accelerating convergence. For feature aggregation, we first propose correlation-aware aggregation (CAA) to model correlations among features of the same lesions or anatomical structures. Second, we develop a dual-path adaptive feature aggregation (DAFA) module: the common path captures stable cross-layer semantics and suppresses redundancy, while the delta path emphasizes subtle differences to strengthen the model’s sensitivity to fine details. Finally, we introduce a residual-gated visual state space module (RG-VSS), which dynamically modulates information flow via a convolution-enhanced residual gating mechanism to refine fused representations. Experiments on diverse datasets demonstrate that our HCDAA-Net outperforms some state-of-the-art (SOTA) approaches. Full article

Produced well flow is controlled through valves placed in the Christmas tree. Being mostly gate-type valves, they isolate the well from the surface when commanded or automatically in an emergency. The reliability of these valves is essential for subsea wells, as maintenance and replacement involve high cost, time, and HSE risks. Their design must withstand harsh conditions such as high temperature, pressure, solid particles, and corrosive environments. However, failures caused by leakage, cold welding, and the erosion of sealing elements are still common. These issues motivated the initial stage of this research, which experimentally showed that replacing the current tungsten carbide (WC) coating with polycrystalline diamond compact (PDC) material reduces friction and wear due to its high hardness and thermal stability. Based on these results, a 3D subsea gate valve model was developed and simulated in Ansys Fluent 2024 R2 under API slurry test conditions using the Oka erosion and Discrete Phase Models. A comparative analysis of WC and PDC coatings for a 5-inch gate valve exposed to a 2% sand slurry (250–400 μm) showed that PDC reduces the erosion depth by 77.6% and extends the valve lifetime by 4.5 times. The findings support the use of PDC for improved erosion resistance in subsea valve applications. Full article

Background. Gestational diabetes mellitus (GDM) induces maternal hyperglycemia, which may alter fetal cardiac structure and function, increasing short- and long-term cardiovascular risks. Purpose. To systematically review the evidence on the fetal cardiac structural and functional effects of GDM, to explore the diagnostic role of novel imaging and biochemical biomarkers, and to summarize the long-term cardiovascular complications associated with GDM. Materials and Methods. A systematic search of PubMed, Scopus, and Cochrane Library was conducted according to the PRISMA guidelines. All studies comparing cardiac outcomes in GDM and non-GDM pregnancies were included. Data on myocardial hypertrophy, diastolic and systolic function, imaging modalities, and biomarkers were extracted and qualitatively synthesized. Results. A total of twelve eligible studies were identified. Fetal cardiac hypertrophy and diastolic and early systolic dysfunction are common among GDM pregnancies and can be detected by dual-gate Doppler and speckle-tracking echocardiography. Abnormalities are observed in indices such as the myocardial performance index, E/A, E/e′ ratios, and global longitudinal and circumferential strain in fetuses and may persist in the neonatal period. Alterations may be more pronounced for the right ventricle compared to the left. Septal hypertrophy is associated with elevated umbilical cord pro-brain natriuretic peptide. The risk of early-onset cardiovascular disease in the progeny of diabetic mothers is 29% higher, as evidenced by population-based cohort data. Conclusions. GDM is linked to fetal cardiac remodeling and an increased long-term cardiovascular risk. Early detection and customized interventions to reduce adverse outcomes may be achieved by integrating advanced echocardiographic techniques and biomarkers into prenatal surveillance. Full article

In the field of remote sensing, accurate semantic segmentation is crucial for applications such as environmental monitoring and urban planning. Effective fusion of multi-modal data is a key factor in improving land cover classification accuracy. To address the limitations of existing methods, such as inadequate feature fusion, noise interference, and insufficient modeling of long-range dependencies, this paper proposes ASGT-Net, an enhanced multi-modal fusion network. The network adopts an encoder-decoder architecture, with the encoder featuring a symmetric dual-branch structure based on a ResNet50 backbone and a hierarchical feature extraction framework. At each layer, Adaptive Weighted Fusion (AWF) modules are introduced to dynamically adjust the feature contributions from different modalities. Additionally, this paper innovatively introduces an alternating mechanism of Learnable Sparse Attention (LSA) and Adaptive Gating Fusion (AGF): LSA selectively activates salient features to capture critical spatial contextual information, while AGF adaptively gates multi-modal data flows to suppress common conflicting noise. These mechanisms work synergistically to significantly enhance feature integration, improve multi-scale representation, and reduce computational redundancy. Experiments on the ISPRS benchmark datasets (Vaihingen and Potsdam) demonstrate that ASGT-Net outperforms current mainstream multi-modal fusion techniques in both accuracy and efficiency. Full article

Battery health monitoring is essential for ensuring the safety, longevity, and efficiency of energy storage systems, particularly in critical applications where reliability is important. Traditional methods for assessing battery degradation, such as Electrochemical Impedance Spectroscopy (EIS), are effective but impractical for large-scale deployment due to their time-intensive nature. This study introduces a novel model-based approach for estimating a critical indicator of battery aging, the internal resistance. Using the NASA battery dataset, specifically focusing on battery numbers 5 and 7 with NCA chemistry, a comprehensive framework that integrates advanced predictive models, i.e., the Random Forest Regressor (RF), the XGBoost Regressor (XGBR), the Gated Recurrent Unit (GRU), and the Long Short-Term Memory (LSTM) networks, was developed. The models were evaluated using common regression metrics, while hyperparameter tuning was performed to optimize performance. The results demonstrated that recurrent neural networks, particularly GRU and LSTM, effectively capture the temporal dependencies inherent in battery aging, offering more accurate state-of-health (SOH) predictions. This approach significantly improves computational efficiency and prediction accuracy, paving the way for practical applications in Battery Management Systems (BMSs). Full article

Power conversion equipment based on high-voltage SiC devices offers significant advantages in efficiency and power density. However, during high-voltage, high-power switching operations, severe electromagnetic interference (EMI) can easily occur. It could cause the false triggering of devices and result in converter failure in severe conditions. This paper firstly establishes a mathematical model and conducts simulation analysis of the conducted common-mode interference path in high-voltage SiC device gate driver circuits. Based on the driver circuit architecture, a modeling method for the common-mode interference conduction network in half-bridge submodules is proposed, clarifying the key factors contributing to high common-mode currents. A low common-mode current design methodology for high-voltage SiC submodules is presented, including driver loop structure optimization, capacitor design, and submodule integration. A highly integrated 3.3 kV SiC-based submodule prototype has been successfully developed, serving as a building block for constructing multilevel modular converters (MMCs). Simulation and experimental results indicate that the amplitude of the common-mode current is primarily influenced by the coupling capacitance of the auxiliary power supply, exhibiting a proportional relationship. The developed SiC submodule achieves high-speed switching at 50 kV/μs under a 2 kV DC bus voltage, with excellent thermal stability and low common-mode current characteristics, validating the effectiveness of the proposed model and design approach. Full article

Engineered wood products have become key sustainable alternatives to conventional building materials, offering strong potential for reducing climate impacts in the construction sector. This review systematically assesses recent life cycle assessment studies on engineered wood products to compare their environmental performance and support low-carbon building practices. The peer-reviewed literature published over the past decade was analyzed for publication trends, geographic focus, and methodological approaches, including goal and scope definition, life cycle inventory, and life cycle impact assessment. Comparative analyses examined climate change impact and key parameters influencing environmental outcomes. Results indicate a steady growth of research in this field, led by China, the United States, and Europe. Volume-based functional units (e.g., 1 m³) are predominant in structural wood studies, while mass-based units are more common for composites. Cradle-to-gate boundaries are most frequently used, and data are primarily drawn from Ecoinvent, Environmental Product Declarations, and regional databases such as GaBi and CLCD. Common impact assessment methods include CML-IA, ReCiPe, and TRACI, with climate change identified as the core impact category. Cross-laminated timber and glue-laminated timber consistently show lower and more stable climate change impacts, while fiberboards exhibit higher and more variable results due to adhesive content and energy-intensive manufacturing. Key factors influencing environmental outcomes include service life, wood species, and material sourcing. The review highlights the need for standardized methodologies and further exploration of emerging products, such as nail-laminated and dowel-laminated timber and laminated bamboo, to improve comparability and inform sustainable design practices. Full article

Background: Inflammation is a common reason for dogs to present to veterinary clinics. Early diagnosis of systemic inflammation is important. Acute phase proteins, like C-reactive protein, are useful but not specific to infection. In human medicine, the intensive care infection score (ICIS) offers a faster, cost-effective alternative using advanced hematological parameters. While ICIS is not available for veterinary use, some components (e.g., neutrophil side fluorescent light) can be measured using analyzers like the Sysmex XN-1000V. Objectives: This study aimed to establish a control group of healthy dogs for the novel parameters neutrophil side fluorescent light (NE-SFL), neutrophil side scattered light (NE-SSC), and neutrophil forward scattered light (NE-FSC) and assess their utility in detecting inflammation in diseases such as sepsis, pyometra, steroid-responsive meningitis-arteritis (SRMA), and idiopathic epilepsy. Methods & Results: Value ranges were calculated based on 21 healthy dogs. Compared to controls, NE-SFL levels were significantly elevated in sepsis, pyometra, and SRMA, while NE-SSC was only elevated in sepsis and pyometra and NE-FSC only in sepsis. No increases were observed in idiopathic epilepsy. Manual gating of the white blood cell differential scattergram was necessary in samples showing high neutrophil toxicity and the presence of bands. Conclusion: NE-SFL and NE-SSC, obtainable from routine complete blood count, may serve as novel, accessible markers for inflammation in dogs. Further research is needed to validate their broader diagnostic use. Full article

Street greening is widely recognized as influencing resident well-being and housing prices, and street-view imagery provides a fine-grained data source for quantifying urban microenvironments. However, existing research predominantly relies on single indicators such as the Green View Index (GVI) and overall green coverage/volume lacking a systematic analysis of how the hierarchical structure of trees, shrubs, and grass relates to housing prices. This study examines the high-density block context of Tianjin’s six urban districts. Using the Street Greening Space Structure (SGSS) dataset to construct greening structure configurations, we integrate housing-price data, neighborhood attributes, and 13,280 street-view images from the study area. We quantify how “visibility and hierarchical ratios” are capitalized on in the housing market and identify auditable threshold ranges and contextual gating. We propose an urban–forest structural system centered on visibility and hierarchical ratios that links street-level observability to ecosystem services. Employing an integrated framework combining Geographical-XGBoost (G-XGBoost) and SHapley Additive exPlanations (SHAP), we move beyond average effects to reveal structural detail and contextual heterogeneity in capitalization. Our findings indicate that tree visibility G_TVI is the most robust and readily capitalized price signal: when G_TVI increases from approximately 0.06 to 0.12–0.16, housing prices rise by about 8%–10%. Hierarchical structure is crucial: balanced tree–shrub ratios and moderate shrub–grass ratios translate “visible green” into functional green. Capitalization effects are environmentally conditioned—more pronounced along corridors with high centrality and accessibility—and are likewise common in dense East Asian metropolises (e.g., Beijing, Shanghai, Seoul, and Tokyo) and rapidly motorizing cities (e.g., Bangkok and Jakarta). These patterns suggest parametric prescriptions that prioritize canopy-corridor continuity and keep ratios within actionable threshold bands. We translate these findings into urban greening strategies that prioritize canopy continuity, under-canopy permeability, and maintainability, providing sustainability-oriented, parameterized guidance for converting urban greening structure into ecological capital for sustainable cities. Full article

This work presents a wideband variable gain low-noise amplifier (VGA-LNA) specifically engineered for medical systems operating in the C frequency band, which require the substantial amplification of low-intensity signals. The proposed design integrates a low-noise attenuator with a low-noise amplifier (LNA), fabricated using 90 nm CMOS technology and leveraging a combined common-source and common-gate topology. The integrated LNA achieved a notable power gain of 29 dB across a broad bandwidth of 2 GHz (6.4–8.4 GHz), maintaining an average noise figure (NF) below 3.14 dB. The design ensures superior impedance matching, demonstrated by reflection coefficients of S11 < −18.14 dB and S22 < −20.23 dB. Additionally, the amplifier exhibits a third-order input intercept point (IIP3) of 21.15 dBm while consuming only 83 mW from a 1.2 V supply voltage. A low-noise attenuator was incorporated at the input side to enable effective gain control through a digitally controlled variable gain, with step sizes ranging from 0.4 to 3.3 dB. This configuration enables a dynamic range of the transmission coefficient (|S₂₁|) from 16 dB to 23 dB, adjustable by 0.4 dB to 3.3 dB with a trade-off in an NF maintained at 6 dB. The VGA-LNA demonstrates exceptional potential for integration into wireless body area networks (WBANs), balancing flexible gain control with stringent performance metrics. Full article

Segmentation of white blood cells is critical for a wide range of applications. It aims to identify and isolate individual white blood cells from medical images, enabling accurate diagnosis and monitoring of diseases. In the last decade, many researchers have focused on this task using U-Net, one of the most used deep learning architectures. To further enhance segmentation accuracy and robustness, recent advances have explored the combination of U-Net with other techniques, such as attention mechanisms and aggregation techniques. However, a common challenge in white blood cell image segmentation is the similarity between the cells’ cytoplasm and other surrounding blood components, which often leads to inaccurate or incomplete segmentation due to difficulties in distinguishing low-contrast or subtle boundaries, leaving a significant gap for improvement. In this paper, we propose GAAD-U-Net, a novel architecture that integrates attention-augmented convolutions to better capture ambiguous boundaries and complex structures such as overlapping cells and low-contrast regions, followed by a gating mechanism to further suppress irrelevant feature information. These two key components are integrated in the Double U-Net base architecture. Our model achieves state-of-the-art performance on white blood cell benchmark datasets, with a 3.4% Dice score coefficient (DSC) improvement specifically on the SegPC-2021 dataset. The proposed model achieves superior performance as measured by mean the intersection over union (IoU) and DSC, with notably strong segmentation performance even for difficult images. Full article

The growing demand from the automotive industry for lightweight, high-performance, and advanced manufacturing techniques for efficient and cost-effective production has accelerated the adoption of fiber-reinforced polymer composites. However, considering the manufacturing complexity of these materials, design remains challenging due to the intricate and interdependent relationships between the process conditions, the part geometry, and the resulting microstructure and quality. This research utilized the Autodesk Moldflow Insight software to design an injection molding process for the manufacturing of discontinuous glass fiber-reinforced polymer parts through computer modeling. A geometrically complex car fender was used as a case study. The effects of various process parameters, particularly gate locations, on the injection-molded parts’ properties (such as the fiber orientation, volumetric shrinkage, and shear rate) were investigated. Multiple injection molding process configurations were designed and simulated, including three, four, and five gates at varying locations. Based on the optimal performance (i.e., low shrinkage, a consistent fiber orientation, and a controllable shear rate), an optimal configuration with four gates at appropriate locations (corresponding to the second gate location set) was identified based on multicriteria decision-making analysis, i.e., volumetric shrinkage of ${8.5}_{-2.2}^{+1.4}\%$, a fiber orientation tensor of 0.927 ± 0.011, and a stable shear rate < 74,324 (1/s). A reduced strain closure model (modified Folgar–Tucker model) was used to predict the glass fiber orientation. A multicriteria decision-making technique, based on similarity ranking with an ideal solution, was employed to optimize the gate location. The simulation results clearly demonstrate that the gate placement is crucial for material behavior during molding and for reducing common defects. The simulation-based injection molding process design for the manufacturing of discontinuous fiber-reinforced polymer parts proposed in this paper can improve the production efficiency, reduce trial-and-error rates, and improve part quality. Full article