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Kirkendall void inhibition through the elimination of diffusivity mismatch assisted by nanocrystalline metal
No full textIn this article, a general method for Kirkendall effect suppression has been proposed that harnesses the diffusivity enhancement effect brought by nanocrystalline metals. Through increasing the fraction of grain boundaries in the fast-diffusing species in the unbalanced diffusion couple, the flux of the slow diffusor can be enhanced through short-circuit diffusion, thus reducing the diffusivity gap, which is the root cause of the Kirkendall effect. Experiments have been performed on both Au-Cu and Au-Ag systems with successful results. A model has been established based on the Au-Cu system that predicts the diffusivity enhancement effect concerning the initial nanocrystalline Cu grain size
Asymmetric influence of urban morphology changes on land surface temperature between daytime and nighttime
No full textUrban development is not solely about the outward expansion of city boundaries but also involves internal renewal within urban areas. While the warming effect of urban expansion is well-documented, the thermal impact of various urban morphology changes remains less clear. Our study investigates the changes in land surface temperature (LST) associated with urbanization, utilizing Local Climate Zone (LCZ) time-series data and satellite-derived LST measurements from three major Chinese urban agglomerations. Time series analysis was applied to examine LST variations over the period from 2003 to 2020. Our research indicates that urban renewal, predominantly characterized by vertical development, exerts an asymmetric effect on urban temperatures: it mitigates urban warming during daytime (-0.13±0.067 °C(mean ± se)) but intensifies it at night (0.20±0.02 °C (mean ± se)). The effect of urban expansion on urban warming is markedly more pronounced during the day (0.55±0.041 °C (mean ± se)) than at night (0.20±0.015 °C (mean ± se)). At the city scale, changes in urban morphology generally contribute to a warming effect, both diurnally and nocturnally. Urban expansion is identified as the primary urban morphology change contributing to the rise in LST. However, the divergent impacts of vertical development, which is likely to account for a larger share of future urbanization, must not be underestimated
Incorporating site suitability and carbon sequestration of tree species into China's climate-adaptive forestation
No full textStrategic selection and precise matching of climate-resilient tree species are crucial for maximizing the mitigation and adaptation potential of Climate-Smart Forestry. However, current forestation plans often overlook species-specific environmental shifts, leading to suboptimal long-term carbon sequestration. Here we developed a climate-adaptive optimization framework to guide tree species selection and planting in China, based on projected habitat suitability and range shifts under future climate scenarios. Utilizing over 200,000 tree records from China's National Forest Inventory (1999–2018), we quantified habitat suitability declines of 12.1%–42.9% for currently dominant plantation species by 2060 due to climate change. By optimizing species-site matching and strategically harvesting timber at peak carbon uptake, we identified 43.2 million hectares suitable for climate-resilient forestation between 2025 and 2060, enabling the planting of approximately 46 billion climate-adapted trees with a total sequestration potential of 3822.6 Tg of carbon—a 28.7% increase compared to unmanaged scenarios. Our study highlights the importance of optimizing adaptive forestation strategies to enhance carbon sequestration under future climate conditions, providing technical guidance for climate-resilient forest management in support of China's net-zero commitment
A critical review of operations research on the operation and maintenance of railway systems
Full text linkThe railway system plays a critical role in modern transportation networks. To ensure its sustainable and reliable operation, conducting efficient operation and maintenance becomes the utmost concern of practitioners. While a growing body of literature has investigated the optimal decision-making for railway system operation (e.g., timetabling, rolling stock scheduling, real-time planning) and maintenance (e.g., time-based, mileage-based, condition-based, opportunistic, and hybrid maintenance) under various settings, a systematic review of their interactions remains lacking. To this end, this paper comprehensively reviews the state-of-the-art research on the operation and maintenance of railway systems, categorizing the relevant literature into four primary research domains: railway system operation, maintenance, and joint operation and maintenance, and fault detection and diagnosis. Potential directions for future research are outlined, including the application of artificial intelligence, integration of multiple railway systems, human-machine collaboration, stochastic modeling of uncertainty and multi-source data acquisition and integration for decision-making in railway systems.published_or_final_versio
‘The World I Witnessed’: Korean Women’s Visits to Europe and their Travel Narratives in the 1940s-1950s
No full textMulti-organ Segmentation from Partially Labeled and Unaligned Multi-modal MRI in Thyroid-associated Orbitopathy
No full textThyroid-associated orbitopathy (TAO) is a prevalent inflammatory autoimmune disorder, leading to orbital disfigurement and visual disability. Automatic comprehensive segmentation tailored for quantitative multi-modal MRI assessment of TAO holds enormous promise but is still lacking. In this paper, we propose a novel method, named cross-modal attentive self-training (CMAST), for the multi-organ segmentation in TAO using partially labeled and unaligned multi-modal MRI data. Our method first introduces a dedicatedly designed cross-modal pseudo label self-training scheme, which leverages self-training to refine the initial pseudo labels generated by cross-modal registration, so as to complete the label sets for comprehensive segmentation. With the obtained pseudo labels, we further devise a learnable attentive fusion module to aggregate multi-modal knowledge based on learned cross-modal feature attention, which relaxes the requirement of pixel-wise alignment across modalities. A prototypical contrastive learning loss is further incorporated to facilitate cross-modal feature alignment. We evaluate our method on a large clinical TAO cohort with 100 cases of multi-modal orbital MRI. The experimental results demonstrate the promising performance of our method in achieving comprehensive segmentation of TAO-affected organs on both T1 and T1c modalities, outperforming previous methods by a large margin. Code will be released upon acceptance
Performance of AAV Networks with Multi-User Interference: A Meta Distribution Analysis
No full textThis letter studies the meta distribution of uplink signal-to-interference ratio (SIR) in unmanned aerial vehicle (UAV) networks with multi-user interference by stochastic geometry theory. In a multiple UAV and multiple ground users (GUs) network with the strongest average received signal strength (RSS) association policy, we model the UAV locations and GU locations as two Poisson point processes and derive the nth moment of the probability of uplink SIR satisfying a given coverage threshold. Since the analytical form of the uplink SIR meta distribution is hard to calculate, we approximate it by the beta distribution to obtain tractable theoretical results. The numerical results validate the accuracy of the approximated formula of the uplink SIR meta distribution in varying network deployment scenarios. Our formula can be used to estimate optimal UAV altitude/density for specific UAV networks, providing guidelines for practical UAV network deployment
Junction-based deep mesa termination for multi-kilovolt vertical β-Ga2O3 power devices
No full textDeep mesa is an effective edge termination widely deployed in high-voltage power devices. However, its effectiveness requires the minimal distance between mesa and electrode edge and is susceptible to charges in the dielectric passivation, posing challenges in practical implementation. Here, we propose a deep mesa termination encapsulated by p-type materials, which functions as a reduced-surface-field (RESURF) structure and enables a wide design and process window. We demonstrate the RESURF-mesa design in vertical Ga3O3 diodes. In this design, a 5 μm deep mesa, which is intentionally not aligned with the anode edge, is encapsulated by p-type nickel oxide (NiO). This termination has been applied to devices on three Ga2O3 wafers with epitaxial doping concentrations ranging from 1.2 × 1016 to 5 × 1016 cm−3, enabling an average one-dimensional junction field of 4.2-4.4 MV/cm in all wafers. Additionally, the diode with 1.2 × 1016 cm−3 doping achieves a specific on-resistance (RON,sp) of 4.05 mΩ·cm2 and a breakdown voltage of 3214 V, resulting in a power figure of merit of 2.55 GW/cm2, which is among the highest in multi-kilovolt β-Ga2O3 diodes. The above results demonstrate the RESURF-mesa termination as a versatile and effective solution for wide bandgap and ultra-wide bandgap power devices
Cold chain routing for product freshness and low carbon emissions: A target-oriented robust optimization approach
No full textAs consumer demand for fresh products continues to rise, the inefficiencies in cold chain logistics have emerged as a pressing issue, resulting in substantial food waste and compromised product quality. Meanwhile, logistics companies face the dual challenge of reducing costs and carbon emissions while ensuring product freshness. In response to these challenges, this paper proposes a novel target-oriented framework that leverages an underperformance riskiness index to optimize cold chain routing decisions. The primary objective is to minimize the risk of not meeting the target freshness level while accounting for costs and carbon emissions. To address the complexity that arises from stochastic arrival times, a linear decision rule is incorporated into the model. The robust counterpart of the problem is reformulated as a mixed-integer linear programming model, which is then solved efficiently using a Benders decomposition approach. Extensive computational experiments are conducted on realistic instances to evaluate the performance of our proposed approach. A comparative analysis with two benchmark models is also performed. The experimental results reveal that our target-oriented robust optimization framework generates high-quality solutions. It effectively reduces both the likelihood and magnitude of violations of the target freshness level, while maintaining relatively low costs and carbon emissions
The Nanosprouts Structural Inhomogeneity of Organic Semiconductors and the Optical Memory Properties
Full text linkStructural inhomogeneities are extensively observed in organic films. Detailed understanding of the crystal lattice packing modes and orientations of these inhomogeneity structures will provide insightful views in revealing the relationship between film morphology and device performance. Herein, this study reports a characterization approach utilizing the lateral force microscopy (LFM) to directly obtain lattice structure information on a commonly used organic small molecular material, i.e., 2,9-Diphenyl-dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophenes (DPh-DNTT) deposited by thermal evaporation. By enhancing sensitivity, the spatial resolution of the LFM approach is optimized. The crystal structure information up to sub-molecular scale can be resolved through the optimization of the LFM test, enabling precise determination of molecular arrangements. Based on the nanosprouts structural inhomogeneity, DPh-DNTT nonvolatile optical memory transistors (OMTs) are developed and the devices demonstrate intrinsic optical memory property with a long retention time of over 1 × 104 s, accompanied by a binary state current ratio greater than 105. Besides proposing the utilization of Kelvin probe force microscope (KPFM) and LFM to identify the charge trapping sites of the OMT, a 16 × 16 flexible active matrix OMT array is fabricated with image processing capability. The devices showcase their potential for applications in the field of machine vision.published_or_final_versio