Revealing Excited States of Rotational Bose-Einstein Condensates

Rotational Bose-Einstein condensates can exhibit quantized vortices as topological excitations. In this study, the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy functional. Various excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics method. Four excitation mechanisms are identified: vortex addition, rearrangement, merging, and splitting. We demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states

DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model

This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous attributes of established SCI techniques and an image generative model, propose a novel structured zero-shot diffusion model, dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies, complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction, especially in the case that current methods struggle to address effectively. Secondly, we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch, thus enabling seamless adaptation of the RGB diffusion model to MSIs. Thirdly, an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process. We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches, surpassing even supervised transformer counterparts across both simulated and real datasets. Our code will be available

Changes in soil chemical properties as affected by pyrogenic organic matter amendment with different intensity and frequency

Pyrogenic organicmatter (PyOM) has long been used as a soil amendment to improve soil physicochemical properties. However, few studies simultaneously investigated both intensities and frequencies of PyOM addition on soil chemical properties of soil base cations, soil pHbuffering capacity (pHBC), and plant availablemicronutrients. In the main food production area of lower Liaohe River Plain in Northeast China, a field manipulation of PyOM addition was initiated in 2013 to examine how the intensities (0, 1%, 3%, and 5% of 0-20 cm soil mass) and frequencies (3% of soil mass applied once versus yearly for 3 years) of PyOM amendment affected soil chemical properties. Higher intensity of PyOM addition significantly increased soil exchangeable Mg (by 24.2%), which was caused by increase of soil pH, soil exchangeable surfaces, and soil organic matter. Plant available Fe, Mn, and Cu were significantly decreased with increasing PyOM addition intensity by up to 39.4%, 50.8%, and 30.0%, respectively, especially under the highest amount of PyOM amendment (5%). This was possibly due to removal of micronutrients with plant biomass or irreversible binding of available micronutrients on PyOM which decreased the extraction efficiency. Under the same amount of PyOM addition (3% in total), higher frequency of PyOM amendment significantly increased soil exchangeable Mg, while lower frequency showed no impact as compared to control plots (CK). Higher frequency of PyOM amendment significantly decreased plant available Mn and Cu as compared to both lower frequency and CK treatments. Both the intensity and frequency of PyOMaddition significantly increased soil pH but showed no influence on soil pHBC. Our results showed that exchangeableMg increased but availableMn and Cu decreasedwith both PyOMamendment intensity and frequency. Even though PyOM amendment could enrich soil base cations, it might cause deficiency of available micronutrients and pose a threat to plant productivity in agroecosystems

Structural Optimization Design of Large Wind Turbine Blade considering Aeroelastic Effect

This paper presents a structural optimization design of the realistic large scale wind turbine blade. The mathematical simulations have been compared with experimental data found in the literature. All complicated loads were applied on the blade when it was working, which impacts directly on mixed vibration of the wind rotor, tower, and other components, and this vibration can dramatically affect the service life and performance of wind turbine. The optimized mathematical model of the blade was established in the interaction between aerodynamic and structural conditions. The modal results show that the first six modes are flapwise dominant. Meanwhile, the mechanism relationship was investigated between the blade tip deformation and the load distribution. Finally, resonance cannot occur in the optimized blade, as compared to the natural frequency of the blade. It verified that the optimized model is more appropriate to describe the structure. Additionally, it provided a reference for the structural design of a large wind turbine blade

Real-time imaging of standing-wave patterns in microresonators

Real-time characterization of microresonator dynamics is important for many applications. In particular it is critical for near-field sensing and understanding light-matter interactions. Here, we report camera-facilitated imaging and analysis of standing wave patterns in optical ring resonators. The standing wave pattern is generated through bi-directional pumping of a microresonator and the scattered light from the microresonator is collected by a short-wave infrared (SWIR) camera. The recorded scattering patterns are wavelength dependent, and the scattered intensity exhibits a linear relation with the circulating power within the microresonator. By modulating the relative phase between the two pump waves, we can control the generated standing waves movements and characterize the resonator with the SWIR camera. The visualized standing wave enables subwavelength distance measurements of scattering targets with nanometer-level accuracy. This work opens new avenues for applications in on-chip near-field (bio-)sensing, real time characterization of photonic integrated circuits and backscattering control in telecom systems

GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy

Graph neural networks (GNNs) have been demonstrated as a powerful tool for analysing non-Euclidean graph data. However, the lack of efficient distributed graph learning (GL) systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, a novel distributed and scalable GL system implemented in vertex-centric graph programming model. GraphTheta is the first GL system built upon distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies, and enables efficient and scalable big graph learning on distributed (virtual) machines with low memory each. To facilitate graph convolution implementations, GraphTheta puts forward a new GL abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution. Moreover, we add support for a new cluster-batched training strategy besides global-batch and mini-batch. We evaluate GraphTheta using a number of datasets with network size ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta obtains comparable or better prediction results than the state-of-the-art GNN implementations, demonstrating its capability of learning GNNs as well as existing frameworks, and can outperform DistDGL by up to $2.02\times$ with better scalability. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task conducted in the literature.Comment: 18 pages, 14 figures, 5 table

Study on the Variation of Terrestrial Water Storage and the Identification of Its Relationship with Hydrological Cycle Factors in the Tarim River Basin, China

The terrestrial water storage anomalies (TWSAs) in the Tarim River Basin (TRB) were investigated and the related factors of water variations in the mountain areas were analyzed based on Gravity Recovery and Climate Experiment (GRACE) data, in situ river discharge, and precipitation during the period of 2002–2015. The results showed that three obvious flood events in 2005, 2006, and 2010 resulted in significant water surplus, although TWSA decreased in the TRB during 2002–2015. However, while the significant water deficits in 2004, 2009, and 2011 were associated with obvious negative river discharge anomalies at the hydrological stations, the significant water deficits were not well consistent with the negative anomalies of precipitation. While the river discharge behaved with low correlations with TWSA, linear relationships between TWSA and climate indices were insignificant in the TRB from 2002 to 2015. The closest relationship was found between TWSA and Pacific Decadal Oscillation (PDO), with correlations of -0.56 and 0.58 during January 2010–December 2015 and during January 2006–December 2009, respectively. Meanwhile, the correlation coefficient between TWSA and El Niño-Southern Oscillation (ENSO) index in the period of April 2002–December 2005 was -0.25, which reached the significant level (p<0.05)

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p &lt; 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients