Coronal condensations caused by magnetic reconnection between solar coronal loops

Employing Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) multi-wavelength images, we report the coronal condensation during the magnetic reconnection (MR) between a system of open and closed coronal loops. Higher-lying magnetically open structures, observed in AIA 171 A images above the solar limb, move downward and interact with the lower-lying closed loops, resulting in the formation of dips in the former. An X-type structure forms at the interface. The interacting loops reconnect and disappear. Two sets of newly-reconnected loops then form and recede from the MR region. During the MR process, bright emission appears sequentially in the AIA 131 A and 304 A channels repeatedly in the dips of higher-lying open structures. This indicates the cooling and condensation process of hotter plasma from ~0.9 MK down to ~0.6 MK, and then to ~0.05 MK, also supported by the light curves of the AIA 171 A, 131 A, and 304 A channels. The part of higher-lying open structures supporting the condensations participate in the successive MR. The condensations without support by underlying loops then rain back to the solar surface along the newly-reconnected loops. Our results suggest that the MR between coronal loops leads to the condensation of hotter coronal plasma and its downflows. MR thus plays an active role in the mass cycle of coronal plasma because it can initiate the catastrophic cooling and condensation. This underlines that the magnetic and thermal evolution has to be treated together and cannot be separated, even in the case of catastrophic cooling.Comment: 10 pages, 6 figure

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product uncertainty quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS

An Experimental Investigation on the Failure Behavior of a Notched Concrete Beam Strengthened with Carbon Fiber-Reinforced Polymer

This paper presents an experiment investigation on the failure behavior of a notched concrete beam reinforced with CFRP, by exploring the influences of the length, thickness, and CFRP bonding methods on the ultimate bearing capacity and failure mode. The interfacial shear stress has first been analytically derived and parametric analyses are then made to predict the failure mode. The experiment observation finds that failure mode significantly depends on CFRP length. The brittle fracture occurs only for nonstrengthened beams; the shear failure I mode mainly occurs when CFRP laminate is 100 mm long; the shear failure II mode mainly occurs when CFRP laminate is 200 mm long; and the delamination failure mode mainly occurs when CFRP laminate is 350 mm long. Meanwhile, the thickness and the bonding methods of CFRP also influence the final failure modes in terms of CFRP length. The measurement on ultimate load shows that an increase in the length of CFRP up to 200 mm significantly improves the bearing capacity of the reinforced beam. A comparison between a theoretical analysis and the experimental observation shows a good agreement in terms of failure modes indicating the accuracy and the validity of the experiment

2,2′-(p-Phenyl­ene)bis­(4,5-dihydro-1H-imidazol-3-ium) bis­(3-nitro­benzoate)

In the title compound, C12H16N4 +·2C7H4NO4 −, the complete 2,2′-(p-phenyl­ene)bis­(4,5-dihydro-1H-imidazol-3-ium) (bib) dication is generated by crystallographic inversion symmetry. The bib cations reside on crystallographic inversion centers, which coincide with the centroids of the respective benzene rings. In the cation, the imidazole ring adopts an envelop conformation with the flap atom displaced by 0.082 (3) Å from the plane through the other ring atoms. In the crystal, the cations and anions are linked through inter­molecular N—H⋯O hydrogen bonds, forming chains running along the a axis. C—H⋯O inter­actions also occur. Weak π–π contacts between the imidazole rings of bib and between the benzene rings of NB [centroid–centroid distances = 3.501 (1) and 3.281 (2) Å, respectively] may further stabilize the structure

Compensatory sweating after restricting or lowering the level of sympathectomy: a systematic review and meta-analysis

OBJECTIVE: To compare compensatory sweating after lowering or restricting the level of sympathectomy. METHOD: A systematic review and meta-analysis were conducted of all randomized controlled trials published in English that compared compensatory sweating after lowering or restricting the level of sympathectomy. The Cochrane collaboration tool was used to assess the risk of bias, and the Mantel-Haenszel odds ratio method was used for the meta-analysis. RESULTS: A total of 11 randomized controlled trials were included, including a total of 1079 patients. Five of the randomized controlled trials studied restricting the level of sympathectomy, and the remaining six studied lowering the level of sympathectomy. CONCLUSIONS: The compiled randomized controlled trial results published so far in the literature do not support the claims that lowering or restricting the level of sympathetic ablation results in less compensatory sweating

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

<p>Abstract</p> <p>Background</p> <p>It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.</p> <p>Results</p> <p>In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods.</p> <p>Conclusion</p> <p>We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site <url>http://home.ustc.edu.cn/~jfxia/hotspot.html</url>.</p

TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning

Taxation and government spending are crucial tools for governments to promote economic growth and maintain social equity. However, the difficulty in accurately predicting the dynamic strategies of diverse self-interested households presents a challenge for governments to implement effective tax policies. Given its proficiency in modeling other agents in partially observable environments and adaptively learning to find optimal policies, Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving dynamic games between the government and numerous households. Although MARL shows more potential than traditional methods such as the genetic algorithm and dynamic programming, there is a lack of large-scale multi-agent reinforcement learning economic simulators. Therefore, we propose a MARL environment, named \textbf{TaxAI}, for dynamic games involving $N$ households, government, firms, and financial intermediaries based on the Bewley-Aiyagari economic model. Our study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI, demonstrating the effectiveness and superiority of MARL algorithms. Moreover, TaxAI's scalability in simulating dynamic interactions between the government and 10,000 households, coupled with real-data calibration, grants it a substantial improvement in scale and reality over existing simulators. Therefore, TaxAI is the most realistic economic simulator, which aims to generate feasible recommendations for governments and individuals.Comment: 26 pages, 8 figures, 12 table