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In this study, a 3D model is developed to simulate multi-hole leakage scenarios in buried pipelines transporting hydrogen-blended natural gas (HBNG). By introducing three parameters—the First Dangerous Time (FDT), Ground Dangerous Range (GDR), and Farthest Dangerous Distance (FDD)—to characterize the diffusion hazard of the gas mixture, this study further analyzes the effects of the number of leakage holes, hole spacing, hydrogen blending ratio (HBR), and soil porosity on the diffusion hazard of the gas mixture during leakage. Results indicate that gas leakage exhibits three distinct phases: initial independent diffusion, followed by an intersecting accelerated diffusion stage, and culminating in a unified-source diffusion. Hydrogen exhibits the first two phases, whereas methane undergoes all three and dominates the GDR. Concentration gradients for multi-hole leakage demonstrate similarities to single-hole scenarios, but multi-hole leakage presents significantly higher hazards. When the inter-hole spacing is small, diffusion characteristics converge with those of single-hole leakage. Increasing HBR only affects the gas concentration distribution near the leakage hole, with minimal impact on the overall ground danger evolution. Conversely, variations in soil porosity substantially impact leakage-induced hazards. The outcomes of this study will support leakage monitoring and emergency management of HBNG pipelines.

With the rapid development of Internet of Things (IoT) technology, Wireless Powered Communication Networks (WPCNs) have emerged as a sustainable solution for powering IoT devices. This paper proposes a Deep Q-Network (DQN)-empowered dynamic resource collaborative management scheme addressing limitations in prior WPCN research. Traditional linear energy harvesting models introduce significant errors when Radio Frequency to Direct Current (RF–DC) conversion exhibits nonlinear saturation effects. We adopt a piecewise nonlinear harvesting model and formulate a multi-objective allocation problem using a Markov Decision Process (MDP) framework. Our objective function maximizes network utility while balancing energy efficiency and Jain’s fairness index. A closed-loop optimization framework integrates Gaussian Process Regression (GPR) for harvest prediction. Theoretical contributions include: (1) convergence proofs for Q-learning under Robbins-Monro conditions; (2) Lyapunov stability analysis ensuring bounded energy queue errors; and (3) O(N) computational complexity scalability. Simulation results for a 30-node network demonstrate that our scheme extends network lifetime by 56.4% (117 to 183 rounds), reduces energy allocation standard deviation by 56.8% (23.7 mJ to 12.3 mJ), improves convergence speed by 53.1% (150 vs. 320 episodes), enhances dynamic adaptability by 66.7% (5 vs. 15 rounds), and increases throughput by 33.33% (80 vs. 60 Mbps). These results provide strong support for large-scale WPCN deployment.

Planar structures for halide perovskite solar cells have recently garnered attention, due to their simple and low-temperature device fabrication processing. Unfortunately, planar structures typically show I–V  hysteresis and lower stable device efficiency compared with mesoporous structures, especially for TiO 2 -based n-i-p devices. SnO 2 , which has a deeper conduction band and higher electron mobility compared with traditional TiO 2 , could enhance charge transfer from perovskite to electron transport layers, and reduce charge accumulation at the interface. Here we report low-temperature solution-processed SnO 2 nanoparticles as an efficient electron transport layer for perovskite solar cells. Our SnO 2 -based devices are almost free of hysteresis, which we propose is due to the enhancement of electron extraction. By introducing a PbI 2 passivation phase in the perovskite layer, we obtain a 19.9 ± 0.6% certified efficiency. The devices can be easily processed under low temperature (150  ∘ C), offering an efficient method for the large-scale production of perovskite solar cells. Planar structured perovskite solar cells often show hysteresis and lower efficiency than mesoporous ones. Jiang  et al.  show that using a SnO 2 electron transport layer improves the performance of planar devices, reporting a certified efficiency of 19.9%, and enables a lower processing temperature.

With the rapid progression of global climate warming, the southern China is facing severe challenges related to extreme precipitation. Based on the multi-model ensembles of CMIP6 simulations, the future changes in extreme precipitation in the southern China at various global warming levels (GWLs) under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 are investigated and the likelihood of rare events like those occurred during April–June 2022 is assessed. As global surface temperatures rise, the southern China is expected to experience more frequent and intense extreme precipitation events compared to those in the recent climate. The changes in magnitudes of precipitation extremes and the likelihood of rare events primarily depend on the GWLs, but they are not very sensitive to the scenarios. Under SSP5-8.5 at 1.5 °C, 2 °C, 3 °C and 4 °C GWLs, the probability of Rx30day similar to the 2022 event increases by 3.64, 3.87, 8.68, and 15.46 times relative to the current climate, respectively. We further evaluated the thermodynamic and dynamic mechanisms driving seasonal mean precipitation changes using the moisture budget equation. The results indicate that changes in moisture flux convergence are mainly caused by thermodynamic effects, while dynamic effects play a negative role. Compared to SSP2-4.5 and SSP5-8.5, the increase in seasonal precipitation under SSP3-7.0 tends to be weaker, related to increased aerosol changes under SSP3-7.0. These findings reveal a strong sensitivity of changes in extreme precipitation in the southern China to GWLs, but less sensitivity to emission scenarios at the same GWLs, providing useful information for climate change mitigation policy in this region.

Quantification and attribution of long-term tropospheric ozone trends are critical for understanding the impact of human activity and climate change on atmospheric chemistry but are also challenged by the limited coverage of long-term ozone observations in the free troposphere where ozone has higher production efficiency and radiative potential compared to that at the surface. In this study, we examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995–2017 using aircraft observations from the In-service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950–250 hPa) by 2.7 ± 1.7 and 1.9 ± 1.7 ppbv per decade on average, respectively, with particularly large increases in the lower troposphere (950–800 hPa) above East Asia, the Persian Gulf, India, northern South America, the Gulf of Guinea, and Malaysia/Indonesia by 2.8 to 10.6 ppbv per decade. The GEOS-Chem simulation driven by reanalysis meteorological fields and the most up-to-date year-specific anthropogenic emission inventory reproduces the overall pattern of observed tropospheric ozone trends, including the large ozone increases over the tropics of 2.1–2.9 ppbv per decade and above East Asia of 0.5–1.8 ppbv per decade and the weak tropospheric ozone trends above North America, Europe, and high latitudes in both hemispheres, but trends are underestimated compared to observations. GEOS-Chem estimates an increasing trend of 0.4 Tg yr−1 of the tropospheric ozone burden in 1995–2017. We suggest that uncertainties in the anthropogenic emission inventory in the early years of the simulation (e.g., 1995–1999) over developing regions may contribute to GEOS-Chem's underestimation of tropospheric ozone trends. GEOS-Chem sensitivity simulations show that changes in global anthropogenic emission patterns, including the equatorward redistribution of surface emissions and the rapid increases in aircraft emissions, are the dominant factors contributing to tropospheric ozone trends by 0.5 Tg yr−1. In particular, we highlight the disproportionately large, but previously underappreciated, contribution of aircraft emissions to tropospheric ozone trends by 0.3 Tg yr−1, mainly due to aircraft emitting NOx in the mid-troposphere and upper troposphere where ozone production efficiency is high. Decreases in lower-stratospheric ozone and the stratosphere–troposphere flux in 1995–2017 contribute to an ozone decrease at mid-latitudes and high latitudes. We estimate the change in tropospheric ozone radiative impacts from 1995–1999 to 2013–2017 is +18.5 mW m−2, with 43.5 mW m−2 contributed by anthropogenic emission changes (20.5 mW m−2 alone by aircraft emissions), highlighting that the equatorward redistribution of emissions to areas with strong convection and the increase in aircraft emissions are effective for increasing tropospheric ozone's greenhouse effect.

The United States is the world’s largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government’s climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a−1 for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.

PurposeA classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Materials and methodsThis retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7 years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5 min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation.Results407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland–Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were −9.46 mm3 and −50.87 mm3, respectively.ConclusionOur DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Background The Bifidobacterium genus is a prominent bacterial population in the gastrointestinal tract. Previous findings suggest that Bifidobacterium is linked to tumor suppression in mouse models of melanoma. Additionally, when combined with the programmed death-ligand 1 (PD-L1) antibody, it can enhance anti-tumor treatment by increasing tumor-specific T-cell responses and promoting infiltration of antigen-specific T cells into tumors. However, there is a lack of studies on Bifidobacterium in esophageal squamous cell carcinoma (ESCC). This study aimed to investigate the potential impact of Bifidobacterium on this cancer type. Methods We examined 213 samples from ESCC patients who underwent tumor resection. The presence of Bifidobacterium was confirmed using quantitative polymerase chain reaction and fluorescent in situ hybridization (FISH). Patient overall survival (OS) was analyzed with Bifidobacterium positivity. Tumor-infiltrating lymphocytes (TILs) were evaluated via hematoxylin and eosin stains, and immunohistochemistry was used to assess programmed death-1 (PD-1), PD-L1, cluster of differentiation 8 (CD8), and forkhead box P3 (FOXP3) expression. Nutritional status was evaluated via computed tomography scans. Results Bifidobacterium positivity showed no correlation with patient OS or TIL levels; however, Bifidobacterium positivity in normal tissue was associated with lower FOXP3 levels, suggesting a potential role in upregulating anti-tumor immune responses. Patients with Bifidobacterium present in peritumor normal tissue exhibited better skeletal muscle area and volume. Conversely, Bifidobacterium positivity in tumor tissue was associated with poorer prognostic nutrition index values, likely due to decreased albumin levels. Conclusion Bifidobacterium can induce the upregulated anti-tumor immune response and is more prevalent in cases with good nutritional status.

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approximately 70–80% to facilitate the subsequent subtle imaging.

In this work, a stable silver nanoparticle (AgNPs) with strong surface plasmon resonance absorption (Abs) signals was synthesized using light-wave technology. In the absence of aptamers, AgNPs can aggregate in a given concentration of salt solution, resulting in significant changes in color. After adding the aptamer (Apt), it was observed that the aptamer can coordinate with AgNPs and adsorb on the surface of AgNPs, thereby maintaining the stability of the nanosol. In the presence of mercury ions (Hg2+), their high-affinity reaction with the aptamer compromised the latter’s protective effect on AgNPs, causing the color of the system to change again. Based on this, a simple and rapid new Abs method for detecting Hg2+ can be constructed. The linear range was 2.5 × 10−3–10.00 μmol/L, and the detection limit (DL) of the system was 2.03 nmol/L.