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Karst reservoirs have always been a key field of oil and gas exploration. However, quantifying the process of meteoric transformation remains a persistent challenge that limits the accuracy of reservoir quality prediction. To explore the controlling factors of meteoric cementation on karst reservoirs, the Majiagou Formation of the Ordos Basin in China was selected as an example. The petrology; carbon, oxygen, and strontium isotopes; and in situ major, trace, and rare earth elements were used, types and origins of calcite cements were analyzed in detail. The results revealed five types of calcite cements (Cal-1~Cal-5), four types of cathodoluminescence (CL) intensities (dull, dull red, deep red, and bright red luminescence), and six types of rare earth element patterns (Pattern-1~Pattern-6). These five types of calcite cements developed in three periods. Cal-1 (transition CL) and Cal-2 (dull CL) were precipitated during the Early Pennsylvanian period, the meteoric freshwater was clean; Cal-3 (transition CL) and Cal-4 (bright red CL) were precipitated at the end of the Late Carboniferous period, the fluids had strong dissolution ability and were polluted by terrigenous debris; Cal-5 (transition CL) was deposited during the burial period, the fluid was pure pore water or groundwater. The control of the cement on the reservoir during the burial period was much weaker than that of meteoric cements. Therefore, explorations of karst reservoirs should be focused on weak cementation during the epigenetic period.

Creating stylized visualization requires going beyond the limited, abstract, geometric marks produced by most tools. Rather, the designer builds stylized idioms where the marks are both transformed (e.g., photographs of candles instead of bars) and also synthesized into a 'scene' that pushes the boundaries of traditional visualizations. To support this, we introduce viz2viz, a system for transforming visualizations with a textual prompt to a stylized form. The system follows a high-level recipe that leverages various generative methods to produce new visualizations that retain the properties of the original dataset. While the base recipe is consistent across many visualization types, we demonstrate how it can be specifically adapted to the creation of different visualization types (bar charts, area charts, pie charts, and network visualizations). Our approach introduces techniques for using different prompts for different marks (i.e., each bar can be something completely different) while still retaining image "coherence." We conclude with an evaluation of the approach and discussion on extensions and limitations., 17 pages

Additional file 2: Supplemental Table 1. Primers used in qPCR.

Additional file 1: Supplementary Fig. 1. Light treatment prevents the onset of senescence in ASCs A-C. The expression of p16, p21, and p53was assessed by qPCR at P3 and P5 AQin ASCs from high fat dietfed C57BL/6 J mice. NFD-P3 as compared a control. E Representative images of SA-β-galactosidase-positive cells. F Senescence was evaluated in terms of SA-β-galactosidase activity and expressed as the ratio of cells protein. Scale bar: 50 μm.

During the current SARS-CoV-2 pandemic, a variety of mutations have accumulated in the viral genome, and currently, four variants of concern (VOCs) are considered potentially hazardous to human society1. The recently emerged B.1.617.2/Delta VOC is closely associated with the COVID-19 surge that occurred in India in the spring of 20212. However, its virological properties remain unclear. Here, we show that the B.1.617.2/Delta variant is highly fusogenic and notably more pathogenic than prototypic SARS-CoV-2 in infected hamsters. The P681R mutation in the spike protein, which is highly conserved in this lineage, facilitates spike protein cleavage and enhances viral fusogenicity. Moreover, we demonstrate that the P681R-bearing virus exhibits higher pathogenicity than its parental virus. Our data suggest that the P681R mutation is a hallmark of the virological phenotype of the B.1.617.2/Delta variant and is associated with enhanced pathogenicity., SARS-CoV-2デルタ株に特徴的なP681R変異は ウイルスの病原性を増大させる. 京都大学プレスリリース. 2021-11-26.

Fritillaria hupehensis, a well-known medicinal perennial herb, is used as an antitussive and an expectorant. Continuous cropping and monoculture cultivation usually negativly affect the growth of F. hupehensis. Compared with the monoculture system, the F. hupehensis-Magnolia officinalis intercropping system significantly increases the yield of F. hupehensis. However, changes in steroidal alkaloid metabolites (the most important bioactive components) and their molecular regulatory mechanisms in F. hupehensis intercropping system remain unclear. We performed comparative transcriptomic and metabolomic analyses of F. hupehensis bulbs grown in monocropping and intercropping systems. A total of 40 alkaloids were identified, including 26 steroidal alkaloids, 4 plumeranes, 3 phenolamines, 1 pyridine alkaloid, and 6 other alkaloids. The results showed that intercropping significantly increased the levels of peimine, peiminine, hupehenine, korseveridine, verticinone N-oxide, delafrine, tortifoline, pingbeinone, puqienine B, puqienine E, jervine, ussuriedine, hydroxymandelonitrile, N-feruloylputrescine, and N-benzylmethylene isomethylamine in F. hupehensis, but decreased the levels of indole, p-coumaroylputrescine, and N-benzylformamide. Transcriptome sequencing identified 11,466 differentially expressed unigenes in F. hupehensis under the intercropping system, of which 5,656 genes were up-regulated and 5,810 genes were down-regulated. We proposed a possible steroidal alkaloid biosynthesis pathway, in which 12 differentially expressed genes were identified. The higher expressions of these genes in the intercropping system positively correlated with the high accumulation of peimine, peiminine, and hupehenine, further validating our proposal. Moreover, the biological processes of oxidative phosphorylation and plant hormone signal transduction, cytochrome P450 enzymes, ATP-binding cassette transporters, and transcription factors may play pivotal roles in the regulation of steroidal alkaloid biosynthesis. This study revealed the underlying molecular mechanisms of intercropping in improving steroidal alkaloids in F. hupehensis at the transcriptome and metabolome levels. These findings provided a theoretical foundation for sustainable development of this ecological planting method.

The VSC-HVDC system can damp the inter-area low-frequency oscillation effectively. To enhance the ability of the VSC-HVDC supplementary control, this article proposes a unified low-frequency oscillation controller design using the robustness theory. The unified control strategy uses both the active power control and reactive power control loop of the VSC to expand the supplementary control dimensions. To design the controller, the TLS-ESPRIT identification method is used to obtain the system small signal model and the oscillation characteristic. Based on the model identified out, the linear matrix inequality method based robust control theory is applied for the controller design, and the robust controllers for active power control and reactive power control loop are both designed to improve the control effect. Finally, the simulation results show that the controller can reach better control effect and the robustness can also be guaranteed.

The B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha)1. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era., A study of SARS-CoV-2 variants examining their transmission, infectivity, and potential resistance to therapies provides insights into the biology of the Delta variant and its role in the global pandemic.

This paper presents a relative position and attitude estimation method using consecutive point clouds without feature extraction. Using this method, the inaccurate state estimation problems for non-cooperative targets caused by the mismatched point pairs or the low tracking accuracy of point cloud features can be resolved. First, point cloud registration is carried out by the transformation of the covariance matrices of the point cloud between two adjacent frames. Meanwhile, the random sample consensus algorithm is employed to reject the mismatched point pairs. Then, pose-graph optimization is adopted to eliminate the accumulated errors of consecutive point cloud registration. Finally, an Extended Kalman Filter is designed to estimate the position, velocity, and angular velocity of the target. The experimental results show that the covariance matrix transform algorithm can achieve the point cloud registration for close roto-translational motions, and the target motion state can be estimated effectively and continuously.

Sub-femtosecond x-ray burst is powerful tool for probing and imaging electronic and concomitant atomic motion in attosecond physics. For years, x-ray source (above 2 keV) had mainly been obtained from X-ray free electron laser (XFEL) or synchrotron radiation, which are high energy consumption, high cost and huge volume. Here we propose a low-cost and small-size method to generate X-ray source. We experimentally obtained high photon energy spectrum (~ 5.2 keV) through both atom and multiple valence state ions using a near-infrared 1.45 {\mu}m driving laser interacting with krypton gas, according to our knowledge, which is the highest photon energy generated through high-order harmonic generation up to now. In our scheme, multi-keV photon energy can be achieved with a relaxed requirement on experimental conditions, and make time-resolved studies more accessible to many laboratories that are capable of producing high energy photon extending to hard x-ray region. Furthermore, our scheme minimizes the influence of X-ray fluorescence process on detection, and can also be utilized to study the quantum-optical nature of high-order harmonic generation., Comment: 11 pages, 4 figures, research article,submitted to Optica

Additional file 2: Table S1. Sequences of primers used in the study.

Additional file 1: Appendix 1. Model-informed consent form.

SARS-CoV-2 Lambda, a variant of interest, has spread in some South American countries; however, its virological features and evolutionary traits remain unknown. In this study, we use pseudoviruses and reveal that the spike protein of the Lambda variant is more infectious than that of other variants due to the T76I and L452Q mutations. The RSYLTPGD246-253N mutation, a unique 7-amino-acid deletion in the N-terminal domain of the Lambda spike protein, is responsible for evasion from neutralizing antibodies and further augments antibody-mediated enhancement of infection. Although this mutation generates a nascent N-linked glycosylation site, the additional N-linked glycan is dispensable for the virological property conferred by this mutation. Since the Lambda variant has dominantly spread according to the increasing frequency of the isolates harboring the RSYLTPGD246-253N mutation, our data suggest that the RSYLTPGD246-253N mutation is closely associated with the substantial spread of the Lambda variant in South America., Graphical Abstract, Kimura et al. reveal the characteristics of SARS-CoV-2 Lambda variant of interest. Lambda spike is more infectious than that of other variants due to the T76I and L452Q mutations. The RSYLTPGD246-253N mutation is responsible for evasion from neutralizing antibodies and further augments antibody-mediated enhancement of infection.

Additional file 2: Appendix 2. SPIRIT checklist.

Additional file 1: Figure S1. Female RAGE deficiency improved glucose and insulin tolerance in normal diet mice. (A) Glucose tolerance tests (GTT) and Area under the curve (AUC) in each group. n = 6 per group. *p < 0.05 vs. female RAGE−/−-HFD-F mice; #p < 0.05 vs. female RAGE−/−-ND-F mice. (B) Insulin tolerance tests (ITT) and AUC in each group. n = 6 per group. *p < 0.05 vs. female RAGE−/−-HFD-F mice; #p < 0.05 vs. female RAGE−/−-ND-F mice. ND; normal diet. All group data are shown as mean ± SEM.

Eliminating ghosting artifacts due to moving objects is a challenging problem in high dynamic range (HDR) imaging. In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images. In the encoder, a context aggregation network and non-local attention block are adopted to optimize multi-scale features and capture both global and local dependencies of multiple low dynamic range (LDR) images. The decoder based on Swin Transformer is utilized to improve the reconstruction capability of the proposed model. Motivated by the phenomenal difference between the presence and absence of artifacts under the field of structure tensor (ST), we integrate the ST information of LDR images as auxiliary inputs of the network and use ST loss to further constrain artifacts. Different from previous approaches, our network is capable of processing an arbitrary number of input LDR images. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method by comparing it with existing state-of-the-art HDR deghosting models. Codes are available at https://github.com/pandayuanyu/HSTHdr.

Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed. Deep learning-based techniques have achieved remarkable results in low-light image enhancement (LLIE) but have not been widely applied to star field image enhancement due to the lack of training data. To address this problem, we construct the first Star Field Image Enhancement Benchmark (SFIEB) that contains 355 real-shot and 854 semi-synthetic star field images, all having the corresponding reference images. Using the presented dataset, we propose the first star field image enhancement approach, namely StarDiffusion, based on conditional denoising diffusion probabilistic models (DDPM). We introduce dynamic stochastic corruptions to the inputs of conditional DDPM to improve the performance and generalization of the network on our small-scale dataset. Experiments show promising results of our method, which outperforms state-of-the-art low-light image enhancement algorithms. The dataset and codes will be open-sourced.

A new method of non-destructive sub-surface interfacial characterisation has been developed recently, which can be useful for quality assurance of a buried interface in nanoelectronic devices, such as magnetic random access memory. Since the cell size of these devices have been reducing their sizes, it is important to evaluate the resolution of the non-destructive imaging. A sub nanometric layer of different materials such as W and Pt was grown underneath a capping layer with controlled thickness for the evaluation of their sizes in this study. This provides systematic experimental data to show that the technique is capable to resolve down to approximately 2 nm in the plane, which is sufficient for the device imaging.

Neural conversation generation model has attracted more and more attention in open domain conversation generation. Even though NCG-based conversation generation methods obtain promising results, they tend to generate short, generic and uninformative responses for most inputs. Moreover, training NCG models heavily relies on a large amount of quality paired dialogue corpus which tends to be expensive and difficult. In this paper, a unified Cycle-Seq2Seq model is proposed for fully data-driven Neural Conversation Generation (CS2S-NCG) in open domain. CS2S-NCG has ability to simultaneously learn the relevance of paired dialogue corpus, utilize feedback about the quality of the two generation models and reduce dependency on the amount of paired training dataset. Meanwhile, a joint learning strategy supports enhanced generation by better usage of massive unpaired dialogue corpus. An alternating Joint-EM algorithm is developed to solve this joint model. A series of experiments are conducted to evaluate CS2S-NCG on two automated metrics, as well as with human evaluation. The results demonstrate that CS2S-NCG significantly improves the relevance and diversity of generation results, compared to traditional neural conversation generation models. Furthermore, quantitative results also reveal that conversation generation quality are enhanced by leveraging unpaired dialogue corpus for not only CS2S-NCG but also traditional NCG.

Accurate positioning of brackets is a necessary condition for ideal orthodontic treatment. Traditional bracket bonding technology has certain limitations, such as long operation time and poor accuracy. Indirect bonding technology is a method that bonding brackets on the model through intraoral impression or scanning, and then the brack⁃ ets are accurately bonded to the tooth crowns using a transfer tray. In this article, the progression of transfer trays and adhesive agents, the application of digital technology in indirect bonding technology, indirect bonding for invisible appli⁃ ances, and the prospect of this technology are reviewed. The literature review results show that indirect bonding technolo⁃ gy can locate the bracket accurately, the operation is simple, the patient’ s experience is comfortable, and the clinical effi⁃ ciency can be significantly improved, the photocurable adhesive is an ideal adhesive for indirect bonding technology. With the development of digital technology, indirect bonding technology will be able to locate the brackets with increasing accuracy, thus achieving the "digital precision movement" of the teeth. The bonding technology of invisible appliance ac⁃ cessories is essentially a type of indirect bonding technology, It requires indirect bonding technology and digital technolo⁃ gy to highly fit the needs of computer design accessories for the visual tooth movement and the use of indirect bonding technology to accurately bond accessories, ultimately achieving the desired tooth movement. Indirect bonding technology will play an increasingly important role with the development of digital technology and invisible correction technology.

Additional file 4: Supplementary Figure S1. High-fat diet induces obesity, hyperglycemia, and body weights in mice. Mice were fed high-fat diet (HFD) or normal diet (ND) for 16 weeks, after which (A) GTT, (B) ITT, (C) body weight, and (D) body weight post-transplantation were measured (n=6/group); *P

Additional file 1: Matierals, Parts of experiment section. Figure S1. TEM images of macrophage cell membrane and various nanocarriers. (A) Macrophage cell membrane. (B) Spherical MSNs. (C) FA-MSNR/LMDI@MPCM. (D) FA-MSN/LMDI. Figure S2. TEM images of various nanocarriers. (A) MSNRs. (B) FA-MSNR/LMDI. (C) FA-MSNR/LMDI@MPCM. (D) FA-MSNR/LMDI@MP/MPCM. (E) Spherical MSNs. (F) FA-MSN/LMDI. Figure S3. (A) Chemical structure of MPEG-PAE. (B) 1HNMR spectrum of MPEG-PAE. (C) GPC of MPEG-PAE. Figure S4. Changes in particle sizes of MPEG-PAE by DLS at various pH values. (A) pH 7.4. (B) pH 7.2. (C) pH 7.0. (D) pH 6.8. Figure S5. Flow cytometry measurements of uptake of the four types of nanocarriers by 4T1 cells and macrophages. Figure S6. Tumor sections of tumor-bearing nude mice 2, 4 and 24 h after injection with the four types of nanocarriers. Blue: DAPI; green: CD34; red: nanocarriers. Figure S7. Concentration of DOX in plasma at different time after intravenous injection of free DOX, FA-MSNR/LMDI and FA-MSNR/LMDI@MP/MPCM. Figure S8. Accumulative Dox release curves of FA-MSNR/LMDI (A) and FA-MSNR/LMDI@MPCM (B) containing 400 μg mL−1 ICG at 37 °C, pH 7.4 or pH 6.5, with or without NIR irradiation for 10 min. Figure S9. (A) In vitro hematological analysis of MSNRs with various concentrations. (B) Stability analysis of MSNRs in human blood serum. (C) Cytotoxicity of MSNRs with various concentrations on L-02 cells. (D) Cytotoxicity of MSNRs with various concentrations on 293T cells. Figure S10. Histology stainings of main organs from tumor-bearing nude mice. (A) H&E stainings of the sections of heart, liver, spleen, lung, and kidney from tumor-bearing nude mice after intravenous injection of MSNRs. (B) H&E stainings of the sections of heart, liver, spleen, lung, and kidney from tumor-bearing nude mice after intravenous injection of FA-MSNR/LMDI@MP/MPCM with NIR irradiation. Figure S11. Percent survival for difffferent treatment groups during 50 days. Table S1. Pharmacokinetic parameters of DOX after intravenously administration of the four types of nanocarriers at the DOX dose of 1 mg kg−1 of mouse body weight. Table S2. Liver and kidney Function Test.

A large amount of information has been published to online social networks every day. Individual privacy-related information is also possibly disclosed unconsciously by the end-users. Identifying privacy-related data and protecting the online social network users from privacy leakage turn out to be significant. Under such a motivation, this study aims to propose and develop a hybrid privacy classification approach to detect and classify privacy information from OSNs. The proposed hybrid approach employs both deep learning models and ontology-based models for privacy-related information extraction. Extensive experiments are conducted to validate the proposed hybrid approach, and the empirical results demonstrate its superiority in assisting online social network users against privacy leakage., Comment: IJCAI 2019 Workshop. The 4th International Workshop on Smart Simulation and Modelling for Complex Systems

Additional file 1: Figure 1. Western blot immunoreactive images of airway HMGB1 in PA infected mice in the absence or presence of ODSH. Figure 2. ODSH does not significantly improve intrinsic phagocytic ability of macrophages in CF mice.

Additional file 3: Table S3. Primers used in qPCR.

The joint motion of the space manipulator and the collision between the end-effector and target may cause the attitude disturbance of the base. To minimize these two kinds of disturbances simultaneously, a pre-impact trajectory planning method for a nonredundant Free-Floating Space Manipulator (FFSM) system is proposed. Firstly, using the momentum conservation and nonholonomic characteristics, a transitional state between the initial state and the final capture state is added. This state can achieve the correct capture pose of the end-effector. Thereafter, the particle swarm optimization (PSO) algorithm is employed to optimize the transitional state and parameterized joint trajectory to minimize the base disturbance caused by joint motion and collision pulses. Finally, the proposed method is applied to a 6-DOF FFSM system. The results show that this method can realize the optimal pre-impact trajectory planning, and verify its effectiveness.

The increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE) so as to construct a Tourism-domain Knowledge Graph (TKG), which is helpful to manage tourism information and develop downstream applications such as tourism search engine, recommendation and Q & A. However, the existing TKG is deficient, and there are few open methods to promote the construction and widespread application of TKG. In this paper, we present a systematic framework to build a TKG for Hainan, collecting data from popular tourism websites and structuring it into triples. The data is multi-source and heterogeneous, which raises a great challenge for processing it. So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively. We refer to tourism InfoBox for semi-structured knowledge extraction and leverage deep learning algorithms to extract entities and relations from unstructured travel notes, which are colloquial and high-noise, and then we fuse the extracted knowledge from two sources. Finally, a TKG with 13 entity types and 46 relation types is established, which totally contains 34,079 entities and 441,371 triples. The systematic procedure proposed by this paper can construct a TKG from tourism websites, which can further applied to many scenarios and provide detailed reference for the construction of other domain-specific knowledge graphs.

Because the evolution of modern humans from early mammals has been a continuous process, human genetic diversity at the molecular level should reflect mammalian genetic diversity. To test this prediction, we contrasted the proportion of segregating sites, q , in human data from the 1000 Genome Project with phylogenetic trees of genes in 96 mammalian genomes ranging from human to platypus. The evolvability of a gene in the human population was found to be positively correlated with the long-term molecular evolutionary rate (r = 0.592). Further analysis of human singleton and de novo mutations suggested that natural selection begins shaping intraspecies genetic diversity soon after a mutation’s initial appearance. By introducing the rate of molecular evolution as a predictor of q , species-specific selection on genes can be detected as deviations from predicted values. We analyzed the average deviations of 10,173 disease-related gene sets and detected 33 gene sets likely to be under human species-specific selection. Among them, nine gene sets were related, with highly significant and positive t -values, to substance-related disorders, thus indicating that the accelerated evolution of the natural reward pathway is the most representative adaptation in humans. Eight gene sets were significantly related, with negative t -values, to infectious diseases. This result may reflect co-evolution between pathogens and humans or recent bottlenecks in the human population caused by pandemic infectious diseases. Our findings demonstrate the potential usefulness of this population genomics approach that incorporates the rates of long-term molecular evolution as predictors.

In view of the problem of satellite video point-target tracking, a method of Bayesian classification for tracking with the constraint of motion smoothness is proposed, which named Bayesian MoST. The idea of naive Bayesian classification without relying on any prior probability of target is introduced. Under the constraint of motion smoothness, the gray level similarity feature is used to describe the likelihood of the target. And then, the simplified conditional probability correction model of classifier is created according to the independence assumption Bayes theorem. Afterwards, the tracking target position can be determined by estimating the target posterior probability on the basis of the model. Meanwhile, the Kalman filter, an assistance and optimization method, is used to enhance the robustness of tracking processing. The theoretical method proposed are validated in a number of six experiments using SkySat and JL1H video, each has two segments. The experiment results show that the BMoST method proposed have good performance, the tracking precision is about 90% and tracking trajectory is smoothing. The method could satisfy the needs of the following advanced treatment in satellite video.

Magnesium alloys AZ31B plates and pure titanium TA2 plates were bonded successfully through explosive welding. The microstructure and composition of the interface were examined by optical microscope (OM), scanning electron microscopy (SEM) and energy dispersive spectroscope (EDS). To explore the process of element diffusion between AZ31B and TA2, the composite plates were annealed at different temperatures. Furthermore, the mechanical behavior of the composite plates was evaluated. The results show that straight and wavy interfaces coexist on the bonding area. The element diffusion becomes obvious when heating at 450 and 490 °C for 4 and 8 h. The hardness of AZ31B and TA2 is increased significantly due to the plastic deformation and the strain hardening. The mechanical properties of the composite plates are significantly improved compared with those of base plate AZ31B.

One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure. Traditional computational models of dialogue are of two types: (1) task-oriented dialogue, supported by AI planning models,or simplified planning models consisting of frames with slots to be filled; or (2)search-oriented dialogue where every user turn is treated as a search query that may elaborate and extend current search results. Alexa Prize dialogue systems such as SlugBot must support conversational capabilities that go beyond what these traditional models can do. Moreover, while traditional dialogue systems rely on theoretical computational models, there are no existing computational theories that circumscribe the expected system and user behaviors in the intended conversational genre of the Alexa Prize Bots. This paper describes how UCSC's SlugBot team has combined the development of a novel computational theoretical model, Discourse Relation Dialogue Model, with its implementation in a modular system in order to test and refine it. We highlight how our novel dialogue model has led us to create a novel ontological resource, UniSlug, and how the structure of UniSlug determine show we curate and structure content so that our dialogue manager implements and tests our novel computational dialogue model., arXiv admin note: text overlap with arXiv:1801.01531

Photovoltaic inverter is difficult to operate in standalone mode without robust voltage source, which handicaps the development of renewable solar energy. If the inverter works in voltage control mode, it should balance the power flow between the resources and the loads. Utilizing limited power adjustment ability of photovoltaic arrays can improve this performance, which is effective if the assigned power of photovoltaic inverter is less than the maximum power of the arrays. However, if that premise cannot be satisfied, the PV inverter can be vulnerable to disturbance, where reasonable control and mode switch strategy are necessary in this situation. This paper will introduce the scheme of voltage-controlled photovoltaic generator that utilizes the principle of power conversion in synchronous generator. The control method in the situation that the maximum capability of photovoltaic arrays is less than the assigned power of inverter is designed. The proposed method is verified in simulation and hence the effectiveness is proved, which is practical for improving robustness and stability of microgrid integrated photovoltaic inverter.

Voltage control mode is preferable because of better bus voltage support and low reliance on communication. However, that is difficult to implement on photovoltaic inverter without robust voltage source. Meanwhile, photovoltaic arrays have some power management ability that usually neglected due to the use of MPPT, which in fact damages balanced power flow and a robust and stable grid. As a result, control of dc and ac side of photovoltaic inverter is usually separated and broken, where the dc side is usually supposed as an ideal voltage source and the corresponding relationships are neglected. This paper proposes a control scheme called photovoltaic generator for photovoltaic microgrid without robust voltage source, which imitates the principle of power conversion from mature synchronous generator theory. The scheme focuses on the active participation of solar energy in power management and voltage support. The effectiveness is verified in simulation and experiment.

We present our system, CruzAffect, for the CL-Aff Shared Task 2019. CruzAffect consists of several types of robust and efficient models for affective classification tasks. We utilize both traditional classifiers, such as XGBoosted Forest, as well as a deep learning Convolutional Neural Networks (CNN) classifier. We explore rich feature sets such as syntactic features, emotional features, and profile features, and utilize several sentiment lexicons, to discover essential indicators of social involvement and control that a subject might exercise in their happy moments, as described in textual snippets from the HappyDB database. The data comes with a labeled set (10K), and a larger unlabeled set (70K). We therefore use supervised methods on the 10K dataset, and a bootstrapped semi-supervised approach for the 70K. We evaluate these models for binary classification of agency and social labels (Task 1), as well as multi-class prediction for concepts labels (Task 2). We obtain promising results on the held-out data, suggesting that the proposed feature sets effectively represent the data for affective classification tasks. We also build concepts models that discover general themes recurring in happy moments. Our results indicate that generic characteristics are shared between the classes of agency, social and concepts, suggesting it should be possible to build general models for affective classification tasks., Comment: Workshop on Affective Content Analysis (AffCon) 2019, Workshop of Association for the Advancement of Artificial Intelligence (AAAI) 2019, Hawaii, USA January 2019

The contact force/torque between the end-effector of the space manipulator and the target spacecraft will reduce the efficiency and safety of the capture task. A capture strategy using PD-impedance combined control algorithm is proposed to achieve compliant contact between the chaser and target spacecraft. In order to absorb the impact energy, a spring-damper system is designed at the end-effector, and the corresponding dynamics model is established by Lagrange&rsquo, s equation. Then a PD-impedance control algorithm based on steady-state force tracking error is proposed. Using this method, a compliant contact between the chaser and target spacecraft is realized while considering the dynamic coupling of the system. Finally, the general equation of the reference trajectory of the manipulator end-effector is derived according to the relative velocity and impact direction. The performance of the proposed capture strategy is studied by a co-simulation of MSC Adams and MATLAB Simulink in this paper. The results show that the contact plane at the end-effector of the manipulator can decelerate and detumble the target spacecraft. Besides, the contact force, relative velocity, and angular velocity all decrease to zero gradually, and the final stable state can be maintained for a prescribed time interval.

The human cardiac troponin (hcTn) has been implicated in diverse cardiovascular diseases (CDs). The protein function is regulated by the inter-subunit interaction between the N-terminal domain of hcTnC and the C-terminal switch peptide of hcTnI; disruption of the interaction has been recognized as a potential therapeutic strategy for CDs. Here, we report use of biogenic medicines as small-molecule competitors to directly disrupt the protein–protein interaction by competitively targeting the core binding site (CBS) of hcTnC NTD domain. A multistep virtual screening protocol is performed against a biogenic compound library to identify competitor candidates and competition assay is employed to verify the screening results. Consequently, two compounds Collismycin and Compound e are identified as strong competitors (CC50 100 μM) or no (CC50 = N.D.) potency. The competitor ligands are anchored at the core groove of hcTnC CBS site through aromatic and hydrophobic interactions, while few peripheral hydrogen bonds are formed to further confer specificity for domain–compound recognition. These molecular-level findings would benefit from further in vitro and in vivo studies at cellular and animal levels, which can help to practice the ultimate therapeutic purpose.

Despite the superior physical properties of silver (Ag) and its alloys such as high electrical and thermal conductivities and high ductility, their applications in electronics have been quite limited due to corrosion and tarnishing concerns. To expand their applications in electronic packaging, developing new materials and processes and investigating new materials properties are essential. This PhD research aims to design and grow novel Ag alloys and address the intermetallic compound (IMC) issues associated with Ag wire bonds, followed by producing Ag and alloys in various forms for solid state bonding processes valuable for high temperature electronics.Firstly, the growth of homogeneous Ag solid solution phase with zinc (Zn) is conducted at two different compositions and their mechanical properties are evaluated by tensile test. According to the experimental results, Ag solid solution phase with Zn at either composition show tempered yield strength, high tensile strength and large uniform strain compared to those of Ag. Moreover, the tarnish resistance is improved with the alloying of zinc. Secondly, Ag3Al has been reported to be the weakest part in the Ag-Al wire bonds due to its low toughness and low corrosion resistance. A method to suppress Ag3Al through alloying indium (In) into Ag is devised. The working mechanism is studied through transmission electron microscopy (TEM) and thermodynamic modeling. Reaction kinetics and evolution of defects are further discussed. A new approach for die attachment through solid-state bonding using fine-grained Ag foils is developed. The bonding is conducted at 300 ℃, assisted by low pressure. The strength of the joints far exceeds the requirement specified in military standards. No degradation can be observed after aging test. This approach avoids the oxidation issue on bonding interfaces in present sintered-silver technique. Lastly, an Ag-Ag direct bonding process is invented using in-situ decomposition of silver oxide. The bonding is performed at 210 ℃ under 0.1 torr vacuum and assisted by only 1.4 MPa pressure. Encouragingly, the resulting Ag-Ag joints are strong with only sparse voids in nano-scale. Cross-sectional study shows that the bonding is formed by grain growth across the original interface. No residual oxygen is trapped. This new process is a breakthrough in Ag-Ag bonding at low temperature and low pressure that produce pure Ag joints. Its applications are wide open.The Ag solid solution phases investigated here show great potential not only in wire bonding and other mechanical component in electronic devices but also photonic applications. The novel bonding processes achieved in this study should be highly valuable for applications in advanced electronic and photonic packages where performance and reliability are highly preferred.

In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference. UCSC's effort as one of the funded teams in the 2017 Amazon Alexa Prize Contest has yielded Slugbot, an open domain social bot, aimed at casual conversation. We discovered several challenges specifically associated with both NER and NEL when building Slugbot, such as that the NE labels are too coarse-grained or the entity types are not linked to a useful ontology. Moreover, we have discovered that traditional approaches do not perform well in our context: even systems designed to operate on tweets or other social media data do not work well in dialogue systems. In this paper, we introduce Slugbot's Named Entity Recognition for dialogue Systems (SlugNERDS), a NER and NEL tool which is optimized to address these issues. We describe two new resources that we are building as part of this work: SlugEntityDB and SchemaActuator. We believe these resources will be useful for the research community., Comment: Resources can be found: https://nlds.soe.ucsc.edu/node/56

In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We present our modular system, highlighting our different data sources and how we use the human mind as a model for data management. Additionally we build and employ natural language understanding and information retrieval tools and APIs to expand our knowledge bases. We describe our semistructured, scalable framework for crafting topic-specific dialogue flows, and give details on our dialogue management schemes and scoring mechanisms. Finally we briefly evaluate the performance of our system and observe the challenges that an open domain socialbot faces.

In this investigation Magnesium alloys AZ31B/Aluminium alloys 6061 composite plates were obtained successfully through the method of explosive welding. The effect of annealing on the evolution of interface microstructure and mechanical properties of the composite plates were investigated. The results demonstrated that the AZ31B/AA6061 composite plates were bonding well. On annealing at and above 250 °C, intermetallic compounds of Al3Mg2 and Al12Mg17 were observed to form at the bonding interface. By increasing the annealing temperature, the tensile strength of the composite plates increased firstly, then it was dramatically decreased, while the elongation increased significantly. This behaviour was considered to be due to the diffusion of Mg and Al elements as well as the formation of intermetallic compounds during annealing process. Crack propagation and interface delamination were observed of the composite plates annealed at and above 250 °C. Corresponding fracture mechanisms of the composite plates were also analyzed.

With the increasing environmental pollution and energy shortage, countries around the world pay more and more attention to the development and utilization of clean renewable energy, solar energy because of its wide distribution, recyclable, and other characteristics is widely used nowadays, photovoltaic power generation as the main form of solar energy utilization, has also been rapid developed. Photovoltaic gird-connected inverter, as a core component of photovoltaic power generation system, the choice of its topology and control strategy plays a decisive role on the performance of photovoltaic power generation system. This report mainly discusses the topology and principle of this circuit, after that, a high performance single-phase two-stage grid-connected inverter with maximum power point tracking has been modeled and simulated successfully. At the first paragraph, background and some basic knowledge of solar photovoltaic technology are introduced. And then at the second paragraph, literature review has been made to demonstrate some previous research in this area. The third paragraph is the most important part of this report, the model and principle of each part of the inverter has been illustrated separately and what is more, the maximum power point tracking is introduced and implemented in the DC/DC boost converter successfully to ensure the efficiency of the PV panel. Certainly, the results of this inverter will be shown in the paragraph four including the output P-V curve of the PV panel, which shows the work of MPPT, output voltage of the DC/DC boost converter and grid current and so on. Finally, the conclusion and future work will be given in the last paragraph. Master of Science (Power Engineering)