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A robust and efficient non-precious metal catalyst for hydrogen evolution reaction is one of the key components for carbon dioxide-free hydrogen production. Here we report that a hierarchical nanoporous copper-titanium bimetallic electrocatalyst is able to produce hydrogen from water under a mild overpotential at more than twice the rate of state-of-the-art carbon-supported platinum catalyst. Although both copper and titanium are known to be poor hydrogen evolution catalysts, the combination of these two elements creates unique copper-copper-titanium hollow sites, which have a hydrogen-binding energy very similar to that of platinum, resulting in an exceptional hydrogen evolution activity. In addition, the hierarchical porosity of the nanoporous copper-titanium catalyst also contributes to its high hydrogen evolution activity, because it provides a large-surface area for electrocatalytic hydrogen evolution, and improves the mass transport properties. Moreover, the catalyst is self-supported, eliminating the overpotential associated with the catalyst/support interface. Investigations into non-precious metal catalysts for hydrogen evolution are ongoing. Here, the authors report a hierarchical, nanoporous copper-titanium electrocatalyst, and demonstrate that it catalyses hydrogen production at twice the over-all rate of commercial platinum-based catalysts.

Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of U-Net to residual mapping. Then, in order to solve the problem of the network that would produce semantic differences when directly concentrated with low-level and high-level features, we use dense skip pathways to reuse feature maps of different layers as inputs for concatenate neural networks feature layers from different depths. Finally, according to the characteristics of precipitation clouds, the pooling layer of U-Net was replaced by a convolution operation to realize the detection of small precipitation clouds. It was experimentally concluded that the Pixel Accuracy (PA) and Mean Intersection over Union (MIoU) of the improved U-Net on the test set could reach 0.916 and 0.928, the detection of precipitation clouds over Tibet were well actualized.

Niobium, a 4d transition metal, has the highest superconducting transition temperature (Tc=9.2K) of any elemental superconductor as type II superconductor with coherent length, ς approximately that of the penetration length, λ. Pure niobium is grey in color and very soft, which makes this metal easily fabricable into different shapes for superconducting radio- frequency (SRF) cavities. Such cavities are used in some modern accelerators (SNS, CEBAF, XFEL), and are intended for usage in the next generation of particle accelerators, such as ILC. Since the crucial part of the cavities is top 100 nm of Nb near the inner cavity surface, considering the penetration depth is around 40 nm, it has attracted more and more attention in improving the surface process for optimizing the performance of the cavities. Nowadays, the main treatment of the Nb surface includes electro polishing (EP), buffered chemical polishing (BCP), high temperature baking (800 °C, 1000 °C and 1200 °C) and mild baking (120 °C). Firstly, the two half cells are welded together and the weld line is quite rough; there exists a lot of visible pits and defects on the inner shell of cavities. In this Ph.D. thesis, novel techniques in a scanning transmission electron microscope (STEM) that can be used to analyze the atomic scale structure-property relationship, both at room tem- perature and high/LN 2 temperature, are explored. Specifically, by using correlated Z-contrast imaging and electron energy loss spectrum (EELS), the structure, composition and bonding can be characterized directly on the atomic scale, also, light atoms, like H, O and C, are visible in ABF images. For the examining the defect behavior on the cavity surface, heating and cold stages are involved to simulate the baking treatment and low-temperature environments. These studies will serve as an important reference for qualifying different surface treatments to further improve SRF cavities' performance. The experimental results were obtained using JEOL JEM-ARM200CF STEM/TEM, having a cold-field emission gun and being operated at 200 kV. It is equipped with a probe-side Cs corrector, multiple imaging detectors (HAADF, LAADF, ABF, BF) and spectrometers (Gatan Infina EELS, Oxford Instruments XMAX EDS). This setup can achieve spatial resolution better than 70 pm and energy resolution 0.35 eV. Utilizing STEM imaging technologies, the crystal structure of Nb and even light impurities are visualized in HAADF and ABF images. Atomic- resolution EELS contains information about the local density of occupied states as the physical principle behind EELS relates to the interaction of the fast electrons with the sample to cause either collective excitations of electrons (plasmons), or discrete transitions between atomic energy levels. The study for different Nb oxides establishes a set of methodologies to quantify the Nb cavity surface oxidation state based on low-loss/core-loss EELS. Oxygen K-edge split due to orbital hybridation and Nb-M peak chemical shift work well for identifying the Nb valence in oxide. Using this method, the surface oxidation state of Nb is studied, and the effects of oxygen diffusion during the mild baking process is revealed. I suggest that this diffusion may act as an important reason for the observed Q-slope in high field region. Considering that the SRF cavities are operated inside liquid helium vessels, the behavior of surface impurity at low temperature draws more and more attention. Since NbH is conducting material with a transition temperature of 150 K and hydrogen can easily concentrate near the surface, NbH is regarded as the key for the observed Q-disease at low temperature. But the difficulty of studying Nb hydride in a TEM is obvious: the light atom (for hydrogen, Z=1) is almost impossible to visualize in STEM images; the only hydrogen peak in EELS is the H K-edge which is located at 12 eV and it is easily covered by tail of zero-loss peak or plasmon peaks. The second part of my research starts with a study of different NbH superlattices using electron beam diffraction patterns, and then careful low-loss EELS measurements to identify hydrogen concentration at the Nb cavity surface. All of these results provide strong evidence for the existence of hydrogen near the cavity surface, the diffusion of hydrogen into bulk Nb atLN2 temperature, and the relationship between hydrogen segregation and local defects. The last part of the thesis focuses on the surface deformation caused by local strain. Local strain is a common problem of Nb cavity fabrication. Nb carbon layers and particles form at the cavity surface after strain tests, and inside of such particles, smaller dislocations are found which exhibit high strain center and higher oxygen concentration. It is clear that the impurities of light atoms is unavoidable during the cavity manufacturing process, oxide is the dominant impurity and it forms a distinguishable amorphous layer around 5 nm in thickness, hydrides are present following the oxide layer and can diffusion into Nb matrix more than 20 nm. Undoubtedly, these impurities will reduce the cavities' performance, and it will be necessary to find more effective methods for post-production cavity treatments to obtain a smoother and cleaner surface. Another problem, local strain, will effect the surface structure and introduce grain boundaries and other extended defects. Potentially, these defects may interact with surface impurities, correspondingly, the hydrogen segregation increases the mobility of the defects. Such positive correlation will accelerate the degeneration of the surface structure and finally lead to catastrophic effect on the local superconductivity. In summary, various impurities of Nb are investigated with atomic resolution. Methodologies for quantifying Nb oxides and hydrides are developed. Direct observation of hydrogen atoms is realized in ABF images at room temperature, and can also serve as a promising method to identify different hydrides in Nb bulk at LN2 temperature if the cold stage is stable enough. My work on the local strain of Nb cavities points out that Nb carbides play a significant role in the performance of SRF cavities at low temperature and intermediate to high fields.

Multi-die FPGAs are crucial components in modern computing systems, particularly for high-performance applications such as artificial intelligence and data centers. Super long lines (SLLs) provide interconnections between super logic regions (SLRs) for a multi-die FPGA on a silicon interposer. They have significantly higher delay compared to regular interconnects, which need to be minimized. With the increase in design complexity, the growth of SLLs gives rise to challenges in timing and power closure. Existing placement algorithms focus on optimizing the number of SLLs but often face limitations due to specific topologies of SLRs. Furthermore, they fall short of achieving continuous optimization of SLLs throughout the entire placement process. This highlights the necessity for more advanced and adaptable solutions. In this paper, we propose LEAPS, a comprehensive, systematic, and adaptable multi-die FPGA placement algorithm for SLL minimization. Our contributions are threefold: 1) proposing a high-performance global placement algorithm for multi-die FPGAs that optimizes the number of SLLs while addressing other essential design constraints such as wirelength, routability, and clock routing; 2) introducing a versatile method for more complex SLR topologies of multi-die FPGAs, surpassing the limitations of existing approaches; and 3) executing continuous optimization of SLLs across the whole placement stages, including global placement (GP), legalization (LG), and detailed placement (DP). Experimental results demonstrate the effectiveness of LEAPS in reducing SLLs and enhancing circuit performance. Compared with the most recent state-of-the-art (SOTA) method, LEAPS achieves an average reduction of 43.08% in SLLs and 9.99% in HPWL, while exhibiting a notable 34.34\\(\\times\\) improvement in runtime.

Packing is a required step in a typical FPGA CAD flow. It has high impacts to the performance of FPGA placement and routing. Early prediction of packing results can guide design optimization and expedite design closure. In this work, we propose an imbalanced large graph learning framework, ImLG, for prediction of whether logic elements will be packed after placement. Specifically, we propose dedicated feature extraction and feature aggregation methods to enhance the node representation learning of circuit graphs. With imbalanced distribution of packed and unpacked logic elements, we further propose techniques such as graph oversampling and mini-batch training for this imbalanced learning task in large circuit graphs. Experimental results demonstrate that our framework can improve the F1 score by 42.82% compared to the most recent Gaussian-based prediction method. Physical design results show that the proposed method can assist the placer in improving routed wirelength by 0.93% and SLICE occupation by 0.89%.

Mutations in the human copper/zinc superoxide dismutase 1 （hSODI） gene cause familial amyotrophic lateral scle- rosis （ALS）. It remains unknown whether large animal models of ALS mimic more pathological events seen in ALS patients via novel mechanisms. Here, we report the generation of transgenic pigs expressing mutant G93A hSOD1 and showing hind limb motor defects, which are germline transmissible, and motor neuron degeneration in dose- and age-dependent manners. Importantly, in the early disease stage, mutant hSODI did not form cytoplasmic inclusions, but showed nuclear accumulation and ubiquitinated nuclear aggregates, as seen in some ALS patient brains, but not in transgenic ALS mouse models. Our findings revealed that SOD1 binds PCBP1, a nuclear poly（rC） binding protein, in pig brain, but not in mouse brain, suggesting that the SOD1-PCBP1 interaction accounts for nuclear SOD1 accu- mulation and that species-specific targets are key to ALS pathology in large mammals and in humans.

Serum tumor markers carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cytokeratin 19 fragment (CYFRA21-1) and squamous-cell carcinoma-related antigen (SCC-Ag) are routinely used for monitoring the response to chemotherapy or targeted therapy in advanced-stage non-small cell lung cancer (NSCLC), however their role in immunotherapy remains unclear. The aim of this study was to investigate whether dynamics of these serum markers were associated with the efficacy and prognosis of Chinese late-stage NSCLC patients treated with programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors. We initiated a longitudinal prospective study on advanced NSCLC patients treated with PD-1/PD-L1 inhibitors in Chinese PLA general hospital (Beijing, China). Blood samples of baseline and after 6 weeks' treatment were collected. CT scan were used by all patients to evaluate treatment efficacy according to RECIST 1.1. Serum tumor markers levels were measured with an electrochemical luminescence for SCC-Ag and with a chemiluminescent microparticle immunoassay for serum CEA, CA125, and CYFRA21-1. At least 20% decreases of the biomarkers from baseline were considered as meaningful improvements after 6 weeks of treatment with immune checkpoint inhibitors (ICIs). Optimization-based method was used to balance baseline covariates between different groups. Associations between serum tumor biomarker improvements and objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were analyzed. A total of 308 Chinese patients with advanced NSCLC were enrolled in the study. After balancing baseline covariates, patients with meaningful improvements in <2 out of 4 biomarkers (CEA, CA125, CYFRA21-1, and SCC-Ag) was ended up with lower ORR (0.08 vs. 0.35, < 0.001), shorten PFS (median: 5.4 vs. 12.5 months, < 0.001), and OS (median: 11.7 vs. 25.6 months, < 0.001) in the total population. Subgroup analysis of patients with adenocarcinoma revealed that patients with meaningful improvements in <2 out of 4 biomarkers had significant lower ORR (0.06 vs. 0.36, < 0.001), shorten PFS (median: 4.1 vs. 11.9 months, < 0.001), and OS (median: 11.9 vs. 24.2 months, < 0.001). So as in patients with squamous cell carcinoma, meaningful improvements in at least 2 out of 4 biomarkers were linked to better ORR (0.42 vs. 0.08, = 0.014), longer PFS (median: 13.1 vs. 5.6 months, = 0.001), and OS (median: 25.6 vs. 10.9 months, = 0.06). The dynamic change of CEA, CA125, CYFRA21-1, and SCC-Ag from baseline have prognostic value for late-stage NSCLC patients treated with PD-1/PD-L1 inhibitors. Decrease of associated biomarkers serum levels were associated with favorable clinical outcomes.

The cultivation and processing of blueberries hold a significant position within the agricultural and food sectors, necessitating precise monitoring of their yield and quality. This study introduces a novel blueberry ripeness and count detection methodology that integrates an attention mechanism with a bi-directional feature pyramid network (BiFPN) within the YOLOv5 framework. The proposed attention mechanism is designed to enhance the YOLOv5 model’s focus on pertinent features while diminishing the influence of non-essential information. This is achieved by substituting the original feature fusion process in YOLOv5 with a bidirectional weighted feature pyramid structure, which facilitates more effective bidirectional feature integration, thereby augmenting the accuracy of blueberry detection.The enhanced model, designated as YOLOv5-CA, demonstrated superior performance with a mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5, achieving a recall of 88.2% and a precision of 88.8%, culminating in an mAP of 91.1%. When the attention mechanism was supplemented with a bidirectional weighted feature pyramid structure, the most efficacious model, YOLOv5-SE + BiFPN, attained an mAP of 90.5%, with recall and precision rates of 88.5% and 88.4%, respectively. This configuration significantly enhanced the model’s capability to discern blueberries against complex backgrounds.Furthermore, the proposed model demonstrates a proficient detection of both the ripeness stages and the quantity of blueberry fruits, providing a foundational application for the development of automated blueberry harvesting techniques in real-world scenarios. The findings of this study lay the groundwork for future advancements in precision agriculture, particularly in the automation of fruit picking processes, by leveraging the potential of attention-based deep learning architectures.

This study was specifically designed to confirm the hypothesis that microRNA-200c (miR-200c) affects the development of cisplatin (DDP) resistance in human gastric cancer cells by targeting zinc finger E-box binding homeobox 2 (ZEB2). A total of 50 gastric cancer tissues and their corresponding normal adjacent tissue samples were collected. Then, the expression levels of miR-200c and ZEB2 in both gastric cancer specimens and cells were detected using the quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemical methods. A dual-luciferase reporter gene assay was conducted to evaluate the effect of miR-200c on the 3′-untranslated region (3′UTR) luciferase activity of ZEB2. SGC7901/DDP cells were transfected with miR-200c mimics and ZEB2 siRNA, respectively. Subsequently, changes in cellular proliferation and apoptosis were detected through the methyl thiazolyl tetrazolium assay and flow cytometric analysis, respectively. We also carried out a western blot analysis assay in order to detect the expression of apoptosis-related genes and ZEB2. miR-200c was significantly downregulated and ZEB2 was significantly upregulated in both gastric cancer tissues and SGC7901/DDP cells when compared with those in normal tissues and SGC7901 cells (P<0.01). The dual luciferase reporter gene assay showed that miR-200c could specifically bind with the 3′UTR of ZEB2 and significantly suppress the luciferase activity by 42% (P<0.01). Upregulation of miR-200c or downregulation of ZEB2 enhanced the sensitivity of SGC7901/DDP cells to DDP. miR-200c was significantly downregulated in both gastric cancer tissues and cells, while the expression of ZEB2 exhibited the opposite trend. Our study further demonstrated that miR-200c could enhance the sensitivity of SGC7901/DDP cells to DDP through targeted regulation of ZEB2 expression in gastric cancer tissues.

Mutations in the human copper/zinc superoxide dismutase 1 (hSOD1) gene cause familial amyotrophic lateral sclerosis (ALS). It remains unknown whether large animal models of ALS mimic more pathological events seen in ALS patients via novel mechanisms. Here, we report the generation of transgenic pigs expressing mutant G93A hSOD1 and showing hind limb motor defects, which are germline transmissible, and motor neuron degeneration in dose- and age-dependent manners. Importantly, in the early disease stage, mutant hSOD1 did not form cytoplasmic inclusions, but showed nuclear accumulation and ubiquitinated nuclear aggregates, as seen in some ALS patient brains, but not in transgenic ALS mouse models. Our findings revealed that SOD1 binds PCBP1, a nuclear poly(rC) binding protein, in pig brain, but not in mouse brain, suggesting that the SOD1-PCBP1 interaction accounts for nuclear SOD1 accumulation and that species-specific targets are key to ALS pathology in large mammals and in humans.