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Ethyl (S)-4-chloro-3-hydroxybutyrate ((S)-CHBE) is an important chiral intermediate in the synthesis of the cholesterol-lowering drug atorvastatin. Studying the use of SpyTag/SpyCatcher and SnoopTag/SnoopCatcher systems for the asymmetric reduction reaction and directed coupling coenzyme regeneration is practical for efficiently synthesizing (S)-CHBE. In this study, Spy and Snoop systems were used to construct a double-enzyme directed fixation system of carbonyl reductase (BsCR) and glucose dehydrogenase (BsGDH) for converting 4-chloroacetoacetate (COBE) to (S)-CHBE and achieving coenzyme regeneration. We discussed the enzymatic properties of the immobilized enzyme and the optimal catalytic conditions and reusability of the double-enzyme immobilization system. Compared to the free enzyme, the immobilized enzyme showed an improved optimal pH and temperature, maintaining higher relative activity across a wider range. The double-enzyme immobilization system was applied to catalyze the asymmetric reduction reaction of COBE, and the yield of (S)-CHBE reached 60.1% at 30 °C and pH 8.0. In addition, the double-enzyme immobilization system possessed better operational stability than the free enzyme, and maintained about 50% of the initial yield after six cycles. In summary, we show a simple and effective strategy for self-assembling SpyCatcher/SnoopCatcher and SpyTag/SnoopTag fusion proteins, which inspires building more cascade systems at the interface. It provides a new method for facilitating the rapid construction of in vitro immobilized multi-enzyme complexes from crude cell lysate. [ABSTRACT FROM AUTHOR]

Pore is one kind of the typical defects in aluminum alloys laser welding. Porosity is an important indicator for evaluating welding quality, and porosity assessment has attracted increasing attention. This paper presents a multi-fidelity deep learning framework (MFDLF) that enables online porosity prediction without post-weld destructive inspection or radioactive detection. In the proposed approach, the maximum temperature on the bottom wall of the keyhole acquired by numerical simulation is used as the data of fidelity 1 (F1), and the coherent optical measurement technology is used to acquire the keyhole depth as the data of fidelity 2 (F2). After extracting the respective four fluctuation characteristics of the multi-fidelity data separately, a sparse auto-encoder (SAE) is used to fuse the four characteristics into an overall feature. Based on the obvious correspondence between porosity and multi-fidelity fusion features, the MFDLF is constructed with tandem two deep belief network (DBN) models, where the former DBN utilizes process parameters to predict the overall feature of F1 data (Feature 1) that is difficult to obtain in real time. Feature 1 is combined with the overall feature of F2 data (Feature 2) that can be obtained online to predict porosity through the latter DBN. The results show that the MFDLF can predict porosity with significantly higher accuracy than the models constructed using only single-fidelity data. [ABSTRACT FROM AUTHOR]

The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient's tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network's attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the diabetes population has grown younger. Therefore, it has become a key problem to make a timely and effective prediction of diabetes, especially given a single data source. Meanwhile, there are many data sources of diabetes patients collected around the world, and it is extremely important to integrate these heterogeneous data sources to accurately predict diabetes. For the different data sources used to predict diabetes, the predictors may be different. In other words, some special features exist only in certain data sources, which leads to the problem of missing values. Considering the uncertainty of the missing values within the fused dataset, multiple imputation and a method based on graph representation is used to impute the missing values within the fused dataset. The logistic regression model and stacking strategy are applied for diabetes training and prediction on the fused dataset. It is proved that the idea of combining heterogeneous datasets and imputing the missing values produced in the fusion process can effectively improve the performance of diabetes prediction. In addition, the proposed diabetes prediction method can be further extended to any scenarios where heterogeneous datasets with the same label types and different feature attributes exist. [ABSTRACT FROM AUTHOR]

The ancient murals of Qutan Temple in Qinghai Province have a very serious loss of paint. Moreover, the main components of the base color paint layer in the paint loss area and the white patterns in the murals are both calcified, which are similar in color and spectral features. Thus, it is difficult to distinguish them by only using spectral features. A method of paint loss area extraction based on 3D residual network with multi-scale feature fusion is proposed. Firstly, the hyperspectral images with paint loss regions were collected by hyperspectral images. They are pre-processed to establish the training data set. Secondly, 3D residual network models are constructed using 3×3×3, 3×3×5 and 5×5×3 convolution kernels to realize the extraction and fusion of spatial and spectral features at different scales of hyperspectral images. The produced mural hyperspectral dataset is used for network training to obtain the prediction model. Finally, the hyperspectral images are input into the trained model to achieve the extraction of paint loss. After comparing different methods, the experimental result shows that the proposed method can improve the extraction accuracy of mural paint loss and serve as a reference for other deteriorations extraction. [ABSTRACT FROM AUTHOR]

The difference in seam squeeze-out antioxidant loss (in terms of standard oxidative induction time, Std-OIT loss) and thickness reduction are evaluated for three different 1.5 mm-thick high density polyethylene (HDPE) geomembranes (GMBs) seamed using a variety of welding parameters and two different wedge welders. Partial squeeze-out antioxidant loss was detected in seams created from each of the three materials examined, with the majority off loss occurring when seam thickness reductions fell between 0.4 mm and 0.8 mm. Seams with thickness reduction exceeding 0.8 mm were found to exhibit greater squeeze-out Std-OIT loss, with near full Std-OIT depletion for one material. Wedge welder size was found to influence this relationship, some seams created with the large wedge welder exhibiting a near full Std-OIT depletion from squeeze-out at approximately 0.6 mm thickness reduction. Variation in seaming pressure and high load melt index (HLMI) were found to shift the degree of thickness reduction a seam may experience for a given welding speed and temperature, with higher seaming pressure and HLMI values generally resulting in greater thickness reduction. Although, for a given welding speed, wedge temperature, and sheet temperature combination, changes in seaming pressure had a limited effect on squeeze-out Std-OIT loss. This paper provides a rational basis into defining a practical 1.5 mm fusion seam thickness reduction criteria based on limiting antioxidant loss within a seam's squeeze-out and also provides a framework for identifying potentially higher risk fusion seams for future research. • First detailed evaluation of relationship between seam squeeze-out antioxidant loss and thickness reduction. • First detailed evaluation of effect of choice of welding parameters and wedge welder. • Provides a rational basis into defining a 1.5 mm fusion seam thickness reduction criteria based on limiting antioxidant loss. • Provides a framework for identifying potentially high risk fusion seams. [ABSTRACT FROM AUTHOR]

Material ejection upon the breakout of a shock wave at a rough surface is a key safety issue for various applications, including pyrotechnics and inertial confinement fusion. For a few years, we have used laser driven compression to investigate microjetting from calibrated grooves in the free surface of shock-loaded specimens. Fast transverse optical shadowgraphy, time-resolved measurements of planar surface and jet tip velocities, and post-shock analysis of some recovered material have provided data over ranges of small spatial and temporal scales, short loading pulses (ns-order), and extremely high strain rates. In the new experiment reported here, picosecond laser irradiation of a thin copper wire generates an ultrashort x-ray burst which is used to radiograph the microjets expanding from plane wedged-shape grooves in tin and copper samples shock-loaded by a longer, nanosecond laser pulse. Such ultrafast radiography provides estimates of the density gradients along the jets and of the total ejected mass at different times after shock breakout. Furthermore, it reveals regions of low density inside the samples deep beneath the grooves, associated with subsurface damage due to tension induced by the interaction of rarefaction waves. Thus, combining this x-ray probe with our former experimental techniques provides a more complete insight into the physics of microjetting at very high loading rates and the ballistic properties of the resulting ejecta. [ABSTRACT FROM AUTHOR]

This paper introduces a complex novel concept and methodology for the creation of personalized biomedical appliances 3D-printed from certified biocompatible photopolymer resin Dental LT Clear (V2). The explained workflow includes intraoral and CT scanning, patient virtualization, digital appliance design, additive manufacturing, and clinical application with evaluation of the appliance intended for patients with cranio-facial syndromes. The presented concept defines virtual 3D fusion of intraoral optical scan and segmented CT as sufficient and accurate data defining the 3D surface of the face, intraoral and airway morphology necessary for the 3D design of complex personalized intraoral and extraoral parts of the orthopedic appliance. A central aspect of the concept is a feasible utilization of composite resin for biomedical prototyping of the sequence of marginally different appliances necessary to keep the pace with the patient rapid growth. Affordability, noninvasiveness, and practicality of the appliance update process shall be highlighted. The methodology is demonstrated on a particular case of two-year-old infant with Pierre Robin sequence. Materialization by additive manufacturing of this photopolymer provides a highly durable and resistant-to-fracture two-part appliance similar to a Tübingen palatal plate, for example. The paper concludes with the viability of the described method and material upon interdisciplinary clinical evaluation of experts from departments of orthodontics and cleft anomalies, pediatric pneumology and phthisiology, and pediatric otorhinolaryngology. [ABSTRACT FROM AUTHOR]

• Crystallization kinetics coupled thermal model for PA12 processing simulation. • Powder densification is considered in melt pool evolution modeling. • Melt pool contractions obtained by simulations and experiments show good consistency. The phase transformation and densification behaviors are two critical research topics in the semi-crystalline polymer Laser Powder Bed Fusion (L-PBF) process. This study first introduces a thermal model incorporating crystallization kinetics to study the temperature evolution and phase transformation behavior during the L-PBF of polyamide-12 (PA12). The model highlights the delayed solidification behavior represented by the overall degree of crystallization compared with the temperature evolution during the cooling stage. A thermo-hydrodynamic model is then developed based on the thermal model to predict densification behavior and investigate the contraction under various processing conditions, incorporating powder densification kinetics. Numerical studies reveal that the contraction of the melt pool lags behind the movement of the laser beam significantly because the densification is not a transient but a continuing process accompanied by the fusion phase transformation. The model is validated by comparing the melt pool contraction obtained by experimental measurement and numerical simulation on single-track sintering. The reasonable thermal interaction during multiple-track simulations is proven to improve the final density noticeably. Finally, a two-laser scanning strategy is proposed to enhance the density of polymer L-PBF components. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-stage fusion framework driven by domain knowledge was proposed for penetration monitoring. • A multi-sensor monitoring platform was built to synchronously collect plasma and welding pool signals. • Information fusion of plasma image, spectrum and pool image was proposed for penetration monitoring. • The correlation between penetration on plasma and molten pool characteristics was investigated. • The prediction model based on a two-stage fusion framework showed a high classification ability of penetration. To solve the problem of industrial application difficulties caused by insufficient depth exploration and poor interpretability of the pure data drive neural network. A two-stage fusion framework driven by domain knowledge was proposed and focused on industrial applications: laser welding penetration monitoring. First, a multi-sensor acquisition system based on plasma and molten pool signals is developed, extracting multiple features from plasma and molten pool images. Two Back Propagation (BP) neural network information fusion models are established in the first fusion stage, leveraging the characteristics of the plasma plume and molten pool front. In the second fusion stage, the Dempster-Shafer (DS) evidence theory is employed to fuse the information from the two BP neural network models. The proposed method, achieving a high accuracy of 98.7 %, is validated by comparing its average accuracy and anti-interference capability with classical Convolutional Neural Network (CNN) and BP neural network models. The physical relationship between input and output is elucidated through an established physical model, enhancing the interpretability and anti-interference capability of the model, and thereby improving the stability and accuracy of the system. [ABSTRACT FROM AUTHOR]

Due to the large dynamic range of real scenes, it is difficult for images taken by ordinary devices to represent high-quality real scenes. To obtain high-quality images, the exposure fusion of multiple exposure images of the same scene is required. The fusion of multiple images results in the loss of edge detail in areas with large exposure differences. Aiming at this problem, this paper proposes a new method for the fusion of multi-exposure images with detail enhancement based on homomorphic filtering. First, a fusion weight map is constructed using exposure and local contrast. The exposure weight map is calculated by threshold segmentation and an adaptively adjustable Gaussian curve. The algorithm can assign appropriate exposure weights to well-exposed areas so that the fused image retains more details. Then, the weight map is denoised using fast-guided filtering. Finally, a fusion method for the detail enhancement of Laplacian pyramids with homomorphic filtering is proposed to enhance the edge information lost by Laplacian pyramid fusion. The experimental results show that the method can generate high-quality images with clear edges and details as well as similar color appearance to real scenes and can outperform existing algorithms in both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develop a multimodal fusion network as a joint coding model, where each single node, layer, and pipeline is represented as a communication channel, and forward propagation equates to information transmission in the channel. This enables us to qualitatively and quantitatively analyze the effect of different fusion approaches. We argue that an optimal fusion architecture is related to essential capacity and allocation based on the source and channel models. To test this multimodal fusion hypothesis, we progressively formulate a series of multimodal models based on our proposed fusion methods and evaluate them on benchmark KITTI and the A2D2 datasets. Our optimal fusion network achieves 85%+ lane line accuracy and 98.7%+ overall accuracy. We conclude that the performance gap with state-of-the-art models demonstrates the potential of our fusion architecture to serve as a new benchmark resource for the multimodal deep learning community. • Review the multimodal fusion from the perspective of information theory. • Qualitative and quantitative analysis on different factors in the fusion network. • Provide a robust lane line segmentation network by fusing LiDAR and camera. [ABSTRACT FROM AUTHOR]

Background: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models. Methods: The redundancy of raw protein is reduced by CD-HIT. The four subsequences are built from protein sequence: one global and three locals. The global subsequence is the entire protein sequence, and these local subsequences are obtained by moving a sliding window with some rules. Using G-gap to extract the features of the above four subsequences, a mixed matrix is constructed as the input of one channel which is composed of three-layer convolutional operating. Additional features are extracted by SCRATCH tool as input of another channel, which is consist of a single convolution in order to find hidden relationships and improve the accuracy of predictor. The outputs of two parallel channels are concatenated as the input of the hidden layer. And the prediction of protein solubility is obtained in the output layer. The best protein solubility prediction model is obtained by doing some comparative experiments of different frameworks. Results: The performance indicators of DDcCNN model (our designed) are as follows: accuracy of 77.82%, Matthew's correlation coefficient of 0.57, sensitivity of 76.13% and specificity of 79.32%. The results of some comparative experiments show that the overall performance of DDcCNN model is better than existing models (GCNN, LCNN and PCNN). The related models and data are publicly deposited at http://www.ddccnn.wang. Conclusion: The satisfactory performance of DDcCNN model reveals that these features and flexible computational methodologies can reinforce the existing prediction models for better prediction of protein solubility could be applied in several applications, such as to preselect initial targets that are soluble or to alter solubility of target proteins, thus can help to reduce the production cost. [ABSTRACT FROM AUTHOR]

Salient object detection aims to identify the most attractive objects from images. However, their boundaries are typically of poor quality when predicted using available methods. One or multiple objects may also be left undetected if the image contains multiple objects. To solve these problems, this article proposes the novel cross refinement network, which consists of a Res2Net‐based backbone network; a fusion network equipped with four convolutional block attention modules and four edge‐salient cross units; and a detection network with an edge enhancement unit and a residual refinement network (RNN). For RNN training, the rough salient maps generated using the DUTS‐TR dataset are treated as a special training dataset. Compared to existing methods, the proposed network can effectively detect all objects as well as improve the boundaries of the detected objects by performing experiments on five benchmark datasets. Based on the experimental results, the proposed network outperforms existing methods both objectively and subjectively. [ABSTRACT FROM AUTHOR]

Considering that fusion pebble beds are probably subjected to the cyclic compression excitation in their future applications, we presented a computational study to report the effect of mechanical cycling on the behaviors of granular matter. The correctness of our numerical experiments was confirmed by a comparison with the effective medium theory. Under the cyclic loads, the fast granular compaction was observed to evolve in a stretched exponential law. Besides, the increasing stiffening in packing structure, especially the decreasing moduli pressure dependence due to granular consolidation, was also observed. For the force chains inside the pebble beds, both the internal force distribution and the spatial distribution of force chains would become increasingly uniform as the external force perturbation proceeded and therefore produced the stress relief on grains. In this case, the originally proposed 3-parameter Mueth function was found to fail to describe the internal force distribution. Thereby, its improved functional form with 4 parameters was proposed here and proved to better fit the data. These findings will provide more detailed information on the pebble beds for the relevant fusion design and analysis. [ABSTRACT FROM AUTHOR]

Binary defocusing technique can effectively break the limitation of hardware speed, which has been widely used in the real-time three-dimensional (3D) reconstruction. In addition, fusion technique can reduce captured images count for a 3D scene, which helps to improve real-time performance. Unfortunately, it is difficult for binary defocusing technique and fusion technique working simultaneously. To this end, our research established a novel system framework consisting of dual projectors and a camera, where the position and posture of the dual projectors are not strictly required. And, the dual projectors can adjust defocusing level independently. Based on this, this paper proposed a complementary decoding method with unconstrained dual projectors. The core idea is that low-resolution information is employed for high-resolution phase unwrapping. For this purpose, we developed the low-resolution depth extraction strategy based on periodic space-time coding patterns and the method from the low-resolution order to high-resolution order of fringe. Finally, experimental results demonstrated the performance of our proposed method, and the proposed method only requires three images for a 3D scene, as well as has strong robustness, expansibility, and implementation. [ABSTRACT FROM AUTHOR]

The ash‐related issues hinder the safe utilization of Zhundong coal, closely relevant to its ash compositions. Previous studies mainly focused on the experimental methods and the selection of ash composition benchmark was restricted to a specific coal sample with limited element types, ignoring the comprehensive elemental interaction. Here, the impacts of eight compositions on ash fusion and crystal phase transformation behaviors were taken into consideration. Moreover, FactSage software was employed to further elucidate the transformation mechanism of ash composition with the increasing temperature. The results showed that the refractory forsterite and merwinite disappeared but more magnesioferrite was present with an increase in ferric oxide content, resulting in the decrease of ash fusion temperatures. The refractory substances, such as forsterite, perovskite, periclase, and larnite, occurred successively with the increasing ratio of basic oxide to acid oxide. The slag content had reached its maximum at 1200°C, up to 90% as the content of CaO was 16%, which was corresponding to that the flowing temperature descended to the lowest point around 1200°C with 16% CaO. With the rising temperature, more slag appeared and the composition species declined, related with the generation of fusible compounds containing Fe and Na at high temperature. The present work can play a guiding role in safe and clean use of Zhundong coal. [ABSTRACT FROM AUTHOR]

Biological organisms can use amorphous precursors to produce inorganic skeletons with continuous structures through complete particle fusion. Synthesizing monoliths is much more difficult because sintering techniques can destroy continuity and limit mechanical strength. We manufactured inorganic monoliths of amorphous calcium carbonate by the fusion of particles while regulating structurally bound water and external pressure. Our monoliths are transparent, owing to their structural continuity, with a mechanical strength approaching that of single-crystal calcite. Dynamic water channels within the amorphous bulk are synergistically controlled by water content and applied pressure and promote mass transportation for particle fusion. Our strategy provides an alternative to traditional sintering methods that should be attractive for constructing monoliths of temperature-sensitive biominerals and biomaterials. [ABSTRACT FROM AUTHOR]

Mesophase pitch is a high-quality precursor for the synthesis of high-performance pitch-based carbon fibers. The mesophase content, texture and softening point are the most important factors for the structure, properties and spinnability of mesophase pitch (MP). Herein, we report a novel approach for the preparation of mesophase pitch with high anisotropic content and low softening point by AlCl 3 catalytic pyrolysis in the presence of isopropyl chloride (PrCl). The variation of anisotropic content and mesophase texture under different PrCl additions and the effect of the introduction of isopropyl on the molecular stacking of aromatic lamellar molecules were investigated. The results confirmed that, except for their role in improving catalytic activity, carbocation co-catalysts can improve the structure and molecular assembly of aromatic molecules through the introduction of alkyl groups. The reaction of isopropyl chloride (PrCl) as a co-catalyst catalyzed pyrolysis of medium-temperature coal pitch at 370 ℃ for 8 h produced a bulk mesophase with a content close to 100 %. Compared to the AlCl 3 system (without PrCl), the AlCl 3 -PrCl system can catalyze the preparation of mesophases at lower temperatures. In addition, it is logical that the introduction of isopropyl group significantly increases the stacking spacing of the mesophase pitch layer molecules, which can effectively improve the mobility of the aromatic layer molecules resulting in the growth of mesophase. The fusion texture and anisotropic content of mesophase pitch were significantly improved by replacing the nucleophilic reagent of the AlCl 3 -catalyzed system. This work provides feasible ideas for the preparation of high-performance coal tar based mesophase pitch. • Isopropyl chloride (PrCl) - aluminum chloride (AlCl 3) catalyzed the preparation of mesophase. • PrCl-15% has 100% mesophase content and Bulk mesophase texture. • Isopropyl not only catalyzes the reaction as a carbon cation, but also introduces methyl groups into pitch to enhance aromatic molecules slip. [ABSTRACT FROM AUTHOR]

It is of great significance to study the effect of multi-enzyme aggregation behavior at the interface on the formation of multi-enzyme complexes and their co-catalytic characteristics, which is helpful for us to design and construct immobilized multi-enzyme complex systems for in vitro synthetic biology. Here, a magnetic microsphere with chelated Ni2+, was prepared to explore the self-assembly characteristics of PduQ-SpyTag (P-T) and Nox-SpyCatcher (N C) on its surface, based on the mixed interaction mode consisting the affinity of His-tag/Ni2+ and covalent binding of SpyTag/SpyCatcher. After studying the effect of saturated or unsaturated adsorption of P-T on the covalent binding between P-T and N C at the interface, a possible multienzyme interaction mechanism for the affinity-assisted covalent self-assembly on the Ni2+ chelating surface was proposed. The time evolution of NADH showed that the immobilized P-T/N-C complex formed by this method and the free P-T/N-C complex exhibited similar synergistic catalytic properties, and presented higher catalytic efficiency than the simple mixing of P-T and N C. The optimal catalytic conditions, stability and reusability of the immobilized multi-enzyme complexes prepared in this study were also discussed by comparing them with free enzymes. In this study, we demonstrate a simple and effective strategy for self-assembling SpyTag/SpyCatcher fusion proteins on the surface of magnetic beads, which is inspiring for the construction of more cascade enzyme systems at the interface. It provides a new method for facilitating the rapid construction of immobilized multi-enzyme complexes in vitro from the crude cell lysis. [ABSTRACT FROM AUTHOR]

To address the limitations of existing 2D human pose estimation methods in terms of speed and lightweight, we propose a method called Lightweight Fusion SimCC (LFSimCC). LFSimCC incorporates two modules: LiteFNet, which enhances multi-scale spatial information fusion, and LKC-GAU, which improves the modeling capability of spatial information. Specifically, LiteFNet utilizes a combination of self-attention mechanism and novel spatial convolution to enable feature maps to capture richer multi-level global feature representations within the network. On the other hand, LKC-GAU enhances SimCC's ability to capture spatial relationships between joints by incorporating a large kernel of convolution and a self-attention mechanism. Furthermore, we design a keypoint information fusion loss (IFL) that enhances the model's sensitivity to information between keypoints in the human body. Experimental results demonstrate that our method is capable of extracting more decisive information and suppressing redundant feature representations, leading to high recognition accuracy and low inference latency. • The design of flip-convolution helps our model to aggregate multi-scale features. • The attention mechanism reconstructs the SimCC coordinate classification structure. • The information fusion loss enhanced the model's sensitivity to the information. • LFSimCC, which has improved accuracy and faster inference, is proposed. [ABSTRACT FROM AUTHOR]

Among various indoor 3D object detection methods, one of which is based on 3D convolution. However, in current 3D convolutional method for indoor 3D object detection, feature fusion is only implemented in a single direction and adjacent layer, which leads to the feature loss during feature propagation and weakens the detection performance. To solve the above problem, a bidirectional multilayer fusion network (BMFN) is proposed to enrich features for prediction. Furthermore, we propose a new network based on BMFN for 3D object detection, BMFN3D. In BMFN3D, the channel and spatial attention mechanisms are introduced into the backbone and head of detection network, so as to enhance the selection of effective feature information. Finally, a new calculation method for regression loss is proposed with the consideration of the 3D spatial information. We validate our method with two indoor datasets of SUN RGB‐D and ScanNet, and achieve state‐of‐the‐art results. [ABSTRACT FROM AUTHOR]

The aim of this case report is to present the root canal treatment of a maxillary left lateral incisor fused to a supernumerary tooth associated with incomplete root development. After thorough chemomechanical debridement of the root canal using 3% sodium hypochlorite and 2% chlorhexidine, aqueous-based calcium hydroxide was used as an intracanal dressing. The apical 3-4 mm of the canal were sealed with mineral trioxide aggregate. The 1-year follow-up radiograph revealed successful apical closure and periapical healing. This case report points out that: (i) A careful clinical and radiographic examination is necessary to establish the diagnosis of fusion with a supernumerary tooth or with a normal tooth. (ii) Even in a fused tooth with an open apex and associated with large periapical radiolucency, careful nonsurgical root canal treatment might result in satisfactory periradicular healing. (iii) The single-visit MTA plug method is an alternative to multi-visit apexification in the treatment of immature teeth with open apex. [ABSTRACT FROM AUTHOR]

Thermal emission spectra mediated by surface phonon polariton are examined by using a theoretical model that accounts for generation processes. Specifically, the acoustic phonon fusion mechanism is introduced to remedy theoretical deficiencies of the near thermal equilibrium treatments. The model clarifies the thermal excitation mechanism of surface phonon polaritons and the energy transfer path under non-zero energy flow. When applied to GaAs and SiC semi-infinite surfaces, the nonequilibrium model predicts that the temperature dependence of the quasi-monochromatic peak can exhibit distinctly different characteristics of either sharp increase or slow saturation depending on the materials, which is in direct contrast with the estimate made by the near-equilibrium model. The proposed theoretical tool can accurately analyze the nonequilibrium steady states, potentially paving a pathway to demonstrate stimulated interaction/emission of thermally excited surface phonon polaritons. [ABSTRACT FROM AUTHOR]

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high. [ABSTRACT FROM AUTHOR]

The present work deals with unsubstituted zinc phthalocyanine (ZnPc), seeking to develop an advanced algorithm for solution-processed ZnPc films. Specifically, it reports on a detailed AFM study of ZnPc crystallites deposited from solution in the binary cyclopentanone–3-dimethylamino-1-propanol system. The crystallization behavior of ZnPc has been radically improved by means of simple aromatic substances added to the dye solution before processing. Varying the nature and quantity of an aromatic additive allows one to change the size, the habit, and even the dimensionality of ZnPc crystallites. Properly modified crystallites are elastic and capable of spontaneous spreading over a solid support. Besides, two alternative kinds of interparticle interactions, namely close agglomeration and seamless fusion, have been explicitly differentiated by AFM phase imaging. The modifying effect of aromatic substances on ZnPc is briefly discussed in terms of supramolecular intercalation of small organic molecules into growing dye crystallites. [ABSTRACT FROM AUTHOR]

The connectivity of mitochondria is regulated by a balance between fusion and division. Many human diseases are associated with excessive mitochondrial connectivity due to impaired Drp1, a dynamin‐related GTPase that mediates division. Here, we report a mitochondrial stress response, named mitochondrial safeguard, that adjusts the balance of fusion and division in response to increased mitochondrial connectivity. In cells lacking Drp1, mitochondria undergo hyperfusion. However, hyperfusion does not completely connect mitochondria because Opa1 and mitofusin 1, two other dynamin‐related GTPases that mediate fusion, become proteolytically inactivated. Pharmacological and genetic experiments show that the activity of Oma1, a metalloprotease that cleaves Opa1, is regulated by short pulses of the membrane depolarization without affecting the overall membrane potential in Drp1‐knockout cells. Re‐activation of Opa1 and Mitofusin 1 in Drp1‐knockout cells further connects mitochondria beyond hyperfusion, termed extreme fusion, leading to bioenergetic deficits. These findings reveal an unforeseen safeguard mechanism that prevents extreme fusion of mitochondria, thereby maintaining mitochondrial function when the balance is shifted to excessive connectivity. SYNOPSIS: The balance between membrane fusion and fission controls mitochondrial connectivity and function. Here, short pulses of membrane depolarization are found to drive an Oma1‐dependent stress response termed 'mitochondrial safeguard' that protects mitochondrial function upon increased mitochondrial connectivity. A mitochondrial stress response adjusts the balance between fusion and fission in response to increased mitochondrial connectivity in mammalian cells.Loss of mitochondrial GTPase Drp1 induces flickering, a repeated and transient decrease of mitochondrial membrane potential.Flickering partially activates the metalloprotease Oma1 and proteolytically inactivates Opa1 GTPase in Drp1‐knockout cells.Unless inactivated, Opa1 along with Mfn1 causes extreme mitochondrial fusion in Drp1‐knockuot cells.Extreme fusion of mitochondria leads to bioenergetic deficits. [ABSTRACT FROM AUTHOR]

Addressing to the problems of few annotated samples and low-quality fused feature in visible and infrared dual-band maritime ship classification, this paper leverages hierarchical features of deep convolutional neural network to propose a dual-band maritime ship classification method based on multilayer convolutional feature fusion. Firstly, the VGGNet model pretrained on the ImageNet dataset is fine-tuned to capture semantic information of the specific dual-band ship dataset. Secondly, the pretrained and fine-tuned VGGNet models are used to extract low-level, middle-level, and high-level convolutional features of each band image, and a number of improved recursive neural networks with random weights are exploited to reduce feature dimension and learn feature representation. Thirdly, to improve the quality of feature fusion, multilevel and multilayer convolutional features of dual-band images are concatenated to fuse hierarchical information and spectral information. Finally, the fused feature vector is fed into a linear support vector machine for dual-band maritime ship category recognition. Experimental results on the public dual-band maritime ship dataset show that multilayer convolution feature fusion outperforms single-layer convolution feature by about 2% mean per-class classification accuracy for single-band image, dual-band images perform better than single-band image by about 2.3%, and the proposed method achieves the best accuracy of 89.4%, which is higher than the state-of-the-art method by 1.2%. [ABSTRACT FROM AUTHOR]

Fusing auxiliary information into ratings has shown promising performance for many recommendation tasks, such as age, sex, vocation of users or actors, director, genre, reviews of movies. However, all above auxiliary information is still sparse and not informative. For movie recommendations, besides the above information, there exists richer information in plot texts, exerting huge impacts on improving the recommendation accuracy. In this paper, we explore effective fusion of movie ratings and plot texts, we propose a deep plot-aware generalized matrix factorization for collaborative filtering model, which effectively combines both ratings and plot texts to implement a generalized collaborative filtering. To verify our proposal, we conduct extensive experiments on two popular datasets, and the results perform better than other state-of-the-art approaches in common recommendation tasks. [ABSTRACT FROM AUTHOR]

The effect of viscosity on the encounter rate of two interacting membranes was investigated by combining a non-equilibrium Fokker-Planck model together with extensive Molecular Dynamics (MD) calculations. The encounter probability and stabilization of transient contact points represent the preliminary steps toward short-range adhesion and fusion of lipid leaflets. To strengthen our analytical model, we used a Coarse Grained MD method to follow the behavior of two charged palmitoyl oleoyl phosphatidylglycerol membranes embedded in a electrolyte-containing box at different viscosity regimes. Solvent friction was modulated by varying the concentration of a neutral, water-soluble polymer, polyethylene glycol, while contact points were stabilized by divalent ions that form bridges among juxtaposed membranes. While a naïve picture foresees a monotonous decrease of the membranes encounter rate with solvent viscosity, both the analytical model and MD simulations show a complex behavior. Under particular conditions, the encounter rate could exhibit a maximum at a critical viscosity value or for a critical concentration of bridging ions. These results seem to be confirmed by experimental observations taken from the literature. [ABSTRACT FROM AUTHOR]

We study the melting transition in crystals of rare gas Ar, Xe, and Kr by the use of extensive Monte Carlo simulations with the Lennard-Jones potential. The parameters of this potential have been deduced by Bernardes in 1958 from experiments of rare gas in the gaseous phase. It is amazing that the parameters of such a popular potential were not fully tested so far. Using the Bernardes parameters, we find that the melting temperature of several rare gas is from 13% to 20% higher than that obtained from experiments. We have throughout studied the case of Ar by examining both finite-size and cutoff-distance effects. In order to get a good agreement with the experimental melting temperature, we propose a modification of these parameters to describe better the melting of rare-gas crystals. [ABSTRACT FROM AUTHOR]

We present the results of a computational investigation of the structure-energy and vibrational properties of alumina under various aggregation states (crystalline, glassy, and liquid) with ab initio procedures. IV-fold, V-fold, and VI-fold oxygen-coordinated aluminum monomeric forms in a dielectric continuum with dielectric constant &eh; = 4.575 were investigated through DFT/B3LYP gas-phase calculations coupled with a Polarized Continuum Model approach and those of the periodical structure D63d (R-3c) which leads to the α-Al2O3 polymorph of alumina, when subjected to symmetry operations, were investigated with the same functional within the LCAO approximation and in the framework of Bloch's theorem. Based on the computed energies and vibrational features, an aggregate of the D63d positively charged cluster [Al12O11]14+ contoured by [AlO4]5- units in an approximate 1:3 proportion to achieve neutrality satisfactorily reproduce the heat capacity of the liquid within experimental uncertainty. The glass is seen as a wrong accretionary form induced by fast cooling rates and subjected to steric forces that locally modify the coordination state of the central atom. Cessation of rotational and translational movements, only partly counterbalanced by acoustic sine-wave-dispersed and excess phonons, gives rise to the huge heat-capacity gap observed at the glass transition (∼5.3R). When cooling rates are sufficiently slow, the accretion around the D63d seeds follows the structural constraints and the heat capacity of α-alumina is almost perfectly reproduced by the 27 Einstein oscillators coupled with the 3 acoustic terms and the anharmonic corrections. [ABSTRACT FROM AUTHOR]

This paper studies the melting behavior of Ag14 cluster employing the instantaneous normal mode (INM) analysis that was previously developed for bimetallic cluster Ag17Cu2. The isothermal Brownian-type molecular dynamics simulation is used to generate atom configurations of Ag14 at different temperatures up to 1500 K. At each temperature, these atomic configurations are then analyzed by the INM technique. To delve into the melting behavior of Ag14 cluster which differs from Ag17Cu2 by the occurrence of an anomalous prepeak in the specific heat curve in addition to the typical principal peak, we appeal to examining the order parameter τ(T) defined in the context of the INM method. Two general approaches are proposed to calculate τ(T). In one, τ(T) is defined in terms of the INM vibrational density of states; in another, τ(T) is defined considering the cluster as a rigid body with its rotational motions described by three orthogonal eigenvectors. Our results for Ag14 by these two methods indicate the mutual agreement of τ(T) calculated and also the consistent interpretation of the melting behavior with the specific heat data. The order parameter τ(T) provides in addition an insightful interpretation between the melting of clusters and the concept of broken symmetry which has been found successful in studies of the melting transition of bulk systems. [ABSTRACT FROM AUTHOR]

Major issue in marine environment imaging is the expulsion of hazy scenes caused by natural phenomena such as absorption and scattering in underwater images. The visual appearance of the underwater image needs to be improved. In this paper, a double stage Gaussian filter (DSGF) and fusion-based approach to enhance the underwater images is proposed. In the proposed two stage process for image fusion, first section deals with applying the DSGF with high, low, and medium sigma values on the individual R, G and B components of input RGB images and second section deals with applying colour component wise contrast limited adaptive histogram followed by median filtering. The outputs of these two sections are fused using principal component analysis fusion technique. Fused image is post-processed using the colour contrast correction for better visibility results. Proposed method is compared with single stage Gaussian filter where only one stage of Gaussian filtering is applied for reduced computation, Adaptive histogram equalization and histogram specification methods. Proposed method results in quality images obtained through gradual intensity improvement and contrast correction process. Qualitative and quantitative analysis are performed on the enhanced underwater images to observe the effectiveness of the algorithm used. The performance measures such as entropy, SSIM, and AMBE unveil the verification of the proposed findings. [ABSTRACT FROM AUTHOR]

In the study, the changes in the mechanical properties of the natural stone samples, which had been obtained from different regions of Turkey, before and after the freeze-thaw test were examined. Within the scope of the study, 17 different natural stone samples were used and, after chemical and mineralogical-petrographical properties were determined. Water absorption at atmospheric pressure, real density, porosity, and uniaxial compressive strength tests were performed before and after freezing-thawing. The freezing and thawing test has been performed using TS EN 12371 standard from -12 °C to +20 °C in 56 cycles (2 cycles = 24 hours). After the test, the samples were subjected to uniaxial compressive strength test in order to identify the changes of the compressive strength values. The results of the physical and mechanical tests on these samples were evaluated by grouping according to chemical and mineralogical-petrographical properties of natural stones. It was aimed to determine which natural stone samples are resistant to freeze-thaw, and can be used in external cladding applications according to the findings obtained from the study. [ABSTRACT FROM AUTHOR]

At present, object detectors based on convolution neural networks generally rely on the last layer of features extracted by the feature extraction network. In the process of continuous convolution and pooling of deep features, the position information cannot be completely transferred backward. This paper proposes a multiscale feature reuse detection model, which includes the basic feature extraction network DenseNet, feature fusion network, multiscale anchor region proposal network, and classification and regression network. The fusion of high-dimensional features and low-dimensional features not only strengthens the model's sensitivity to objects of different sizes but also strengthens the transmission of information, so that the feature map has rich deep semantic information and shallow location information at the same time, which significantly improves the robustness and detection accuracy of the model. The algorithm is trained and tested in Pascal VOC2007 dataset. The experimental results show that the mean average precision of the objects in the dataset is 73.87%. At the same time, compared with the mainstream faster RCNN and SSD detection models, the mean average precision of object detection algorithm based on DenseNet is improved by 5.63% and 3.86%, respectively. [ABSTRACT FROM AUTHOR]

Stacking fault energy (SFE) is related to activating complex high strength and ductility mechanisms such as transformation-induced plasticity and twinning-induced plasticity effects. This type of energy can be estimated by many different methods and its importance is in its ability to predict microstructure and phase transformation behavior when the material is submitted to stress/strain. In order to study the SFE, chemical composition, and microstructure relationships, eleven different welding parameters were chosen to obtain a large range of dilution levels. A new tubular wire electrode of high-manganese steel (21 wt pct Mn) was used as the consumable and an SAE 1012 steel plate (0.6 wt pct Mn) as the base metal in a flux-cored arc welding process. These welding parameters were related to the phases formed and phase transformation behavior in the fusion zone. The SFE of the austenite phase was calculated using a thermodynamic model. The welding parameters produced SFE values in the range of − 5 to 7 mJ/m2. ϵ -martensite and austenite were observed in all samples, but α ′-martensite was only found in those that presented negative SFE values, i.e., those with lower Mn content. Chemical Gibbs Free energy was the component with the most influence on the SFE. Nanoindentation detected the phase transformations during hardness testing for the medium and low dilution levels used, while the high dilution levels presented the highest hardness and modulus of elasticity values, and the lowest elastic and plastic deformation values. The results provide an improved method to develop high-manganese steels with microstructure control through welding parameters. [ABSTRACT FROM AUTHOR]

After two decades of steady growing, symbiotic merger of organocatalysis with emerging electrochemical and photochemical tools are envisioned as hot topics in the coming decade. Here, these trends are discussed in parallel to the implementation of artificial intelligence-based technologies, which anticipate a paradigm shift in catalyst design. [ABSTRACT FROM AUTHOR]

Physical changes in arranged silver nanowires were monitored during progressive heating inside a transmission electron microscope. Using the in-situ experimental method, overall variation of silver nanowires and movement of the silver atoms could be assessed. The physical morphology of silver nanowires was rapidly transformed above 350 °C as they fused with each other, which led to extrusion of the silver atoms. Around 550 °C, silver nanowires were almost fused into one, filling a relatively large void between silver nanowires. However, above 575 °C, the united silver nanowire was completely cut off, starting from the region that was suspected to have defects. For the first time, the fusion of arranged silver nanowires and the configurational changes of silver atoms during heating were visualized, and the migration between silver atoms and the damage mechanism of silver nanowires were assessed. Moreover, the relationship of physical morphology and electrical property of silver nanowires according to the temperature were investigated using the ex-situ experimental method. As silver nanowires started to split at 300 °C, the electrical conductivity deteriorated greatly. Beyond 350 °C, the electrical conductivity was completely lost while silver nanowires disintegrated rapidly, and silver nanowires completely disappeared at 450 °C. [ABSTRACT FROM AUTHOR]

Microstructure and phase transformations were investigated in the weld fusion zone (WFZ) of tungsten inert gas (TIG)-welded IN939 nickel-based superalloy with IN718 and IN625 alloys used as filler metal. The optical and field-emission scanning electron microscopy (FESEM) and also energy dispersive spectroscopy (EDS) were utilized for the microstructural study of the WFZ. The microstructural examination showed that the Nb and Ti elements were microsegregated in the inter-dendrite region, causing a significant concentration gradient between the core and the inter-dendrite region within the WFZ. Observations indicated the formation of the (Nb, Ti) C carbide phases in the solidification process. With a thickness of about 0.2 μm, some plate-like δ phase was observed in the WFZ of both samples upon heat treatment at 750 °C. The results of X-ray diffraction analysis (XRD) confirmed the presence of the δ phase in the WFZ. However, the δ phase disappeared at temperatures beyond 1000 °C. The occurrence of phase transformation during the solidification process was identified by differential thermal analysis (DTA). The WFZ was seen to solidify in the temperature range of 1370°C to 1270°C. [ABSTRACT FROM AUTHOR]

Precise genome editing by systems such as clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) requires high-efficiency homology-directed repair (HDR). Different technologies have been developed to improve HDR but with limited success. Here, we generated a fusion between the Cas9 endonuclease and the Agrobacterium VirD2 relaxase (Cas9-VirD2). This chimeric protein combines the functions of Cas9, which produces targeted and specific DNA double-strand breaks (DSBs), and the VirD2 relaxase, which brings the repair template in close proximity to the DSBs, to facilitate HDR. We successfully employed our Cas9-VirD2 system for precise ACETOLACTATE SYNTHASE (OsALS) allele modification to generate herbicide-resistant rice (Oryza sativa) plants, CAROTENOID CLEAVAGE DIOXYGENASE-7 (OsCCD7) to engineer plant architecture, and generate in-frame fusions with the HA epitope at HISTONE DEACETYLASE (OsHDT) locus. The Cas9-VirD2 system expands our ability to improve agriculturally important traits in crops and opens new possibilities for precision genome engineering across diverse eukaryotic species. Ali, Shami, Sedeek et al. generate a fusion between Cas9 and the VirD2 relaxase (Cas9-VirD2), which combines the functions of both proteins in producing targeted and specific double strand breaks and promoting homology-directed repair. They show the utility of their method by producing herbicide resistant rice. [ABSTRACT FROM AUTHOR]

The fusion of two azacalixphyrin cycles absorbing in the NIR-I domain moves the absorption properties beyond 1000 nm, towards the second biological transparency window (NIR-II). This new type of NIR-II dye was synthesised through the intermediate preparation of a rare example of bis-tetra-azacalix[4]arene where the two macrocycles share a common aromatic unit. [ABSTRACT FROM AUTHOR]

Ned Markosian (2014) has recently defended a new theory of composition, which he calls regionalism : some material objects xx compose something if and only if there is a material object located at the fusion of the locations of xx. Markosian argues that regionalism follows from what he calls the subregion theory of parthood (STP). Korman and Carmichael (2016) agree. We provide countermodels to show that regionalism does not follow from (STP), even together with fourteen potentially implicit background principles. We then show that regionalism does follow from five of those background principles together with (STP) and two additional principles connecting parthood and location, which we call (Overlap) and (Strong Delegation). While the additional principles are not uncontroversial, our conjecture is that many will find them attractive. We conclude by mentioning that (Strong Delegation) fills a previously unnoticed gap in the formal theory of location presented in Parsons (2007). [ABSTRACT FROM AUTHOR]

We determined the freezing point of eight molecular models of water. All models use the charge-on-spring (COS) method to express polarization. The studied models were the COS/G2, COS/G3 [H. Yu and W. F. van Gunsteren, J. Chem. Phys. 121, 9549 (2004)], the COS/B2 [H. Yu, T. Hansson, and W. F. van Gunsteren, J. Chem. Phys. 118, 221 (2003)], the SWM4-DP [G. Lamoureux, A. D. MacKerell, Jr., and B. Roux, J. Chem. Phys. 119, 5185 (2003)], the SWM4-NDP [G. Lamoureux, E. Harder, I. V. Vorobyov, B. Roux, and A. D. MacKerell, Jr., Chem. Phys. Lett. 418, 245 (2006)], and three versions of our model, the BKd1, BKd2, and BKd3. The BKd1 is the original Gaussian model [P. T. Kiss, M. Darvas, A. Baranyai, and P. Jedlovszky, J. Chem. Phys. 136, 114706 (2012)] with constant polarization and with a simple exponential repulsion. The BKd2 applies field-dependent polarizability [A. Baranyai and P. T. Kiss, J. Chem. Phys. 135, 234110 (2011)], while the BKd3 model has variable size to approximate the temperature-density (T-ρ) curve of water [P. T. Kiss and A. Baranyai, J. Chem. Phys. 137, 084506 (2012)]. We used the thermodynamic integration (TI) and the Gibbs-Helmholtz equation to determine the equality of the free energy for liquid water and hexagonal ice (Ih) at 1 bar. We used the TIP4P and the SPC/E models as reference systems of the TI. The studied models severely underestimate the experimental melting point of ice Ih. The calculated freezing points of the models are the following: COS/G2, 215 K; COS/G3, 149 K; SWM4-DP, 186 K; BKd1, 207 K; BKd2, 213 K; BKd3, 233 K. The freezing temperature of the SWM4-NDP system is certainly below 120 K. It might even be that the water phase is more stable than the ice Ih at 1 bar in the full temperature range. The COS/B2 model melts below 100 K. The best result was obtained for the BKd3 model which indicates that correct description of the (T-ρ) curve improves the estimation of the freezing point. We also determined and compared the densities of high-pressure polymorphs of ice for these models. [ABSTRACT FROM AUTHOR]

This work demonstrates a novel two-bit multi-level device structure comprising three phase change material (PCM) layers, separated by SiN thermal barrier layers. This triple PCM stack consisted of (from bottom to top), Ge2Sb2Te5 (GST), an ultrathin SiN barrier, nitrogen-doped GST, another ultrathin SiN barrier, and Ag0.5In0.5Sb3Te6. The PCM layers can selectively amorphize to form 4 different resistance levels ('00,' '01,' '10,' and '11') using respective voltage pulses. Electrical characterization was extensively performed on these devices. Thermal analysis was also done to understand the physics behind the phase changing characteristics of the two-bit memory devices. The melting and crystallization temperatures of the PCMs play important roles in the power consumption of the multi-level devices. The electrical resistivities and thermal conductivities of the PCMs and the SiN thermal barrier are also crucial factors contributing to the phase changing behaviour of the PCMs in the two-bit multi-level PCRAM device. [ABSTRACT FROM AUTHOR]

Motivated by experimental reports of higher-than-bulk melting temperatures in small gallium clusters, we perform first-principles molecular dynamics simulations of Ga20 and Ga20+ using parallel tempering in the microcanonical ensemble. The respective specific heat (CV) curves, obtained using the multiple histogram method, exhibit a broad peak centered at approximately 740 and 610 K-well above the melting temperature of bulk gallium (303 K) and in reasonable agreement with experimental data for Ga20+. Assessment of atomic mobility confirms the transition from solid-like to liquid-like states near the CV peak temperature. Parallel tempering molecular dynamics simulations yield low-energy isomers that are ∼0.1 eV lower in energy than previously reported ground state structures, indicative of an energy landscape with multiple, competing low-energy morphologies. Electronic structure analysis shows no evidence of covalent bonding, yet both the neutral and charged clusters exhibit greater-than-bulk melting temperatures. [ABSTRACT FROM AUTHOR]

Theory predicts, and experiments have shown, that dynamics is faster at glass surfaces than in the bulk, allowing the glass to settle into deeper energy landscape minima, or 'age more.' Is it possible that a glass surface could survive at temperatures where the bulk crystallizes, or that it could remain glassy after the bulk is heated all the way to its melting temperature and re-cooled? We image in real-time and with sub-nanometer resolution the two-state surface dynamics on a cerium-based glass surface, from deep within the glassy regime to above the crystallization temperature. Unlike other surfaces that we have studied, this glass surface remains amorphous even after the bulk re-crystallizes. The surface retains non-crystalline structure and two state dynamics of cooperatively rearranging regions even after heat annealing to just below the bulk melting temperature. The heat-annealed cooperatively rearranging regions are larger than originally, a sign that the surface is well aged. The surface dynamics depends weakly on temperature, showing no sign of the superexponential increase in bulk dynamics expected near Tg. [ABSTRACT FROM AUTHOR]

We study the phase behavior of diblock copolymer melts with one block possessing orientation-dependent segmental interactions using self-consistent field theory. A generalized coarse-grained description is introduced based on the local (polar) orientational order parameter and K, an effective Frank elastic constant for orientational gradients. To explore the role played by orientational interactions in assembly thermodynamics, we apply the theory to two-dimensional melt morphologies for a range of K. As microphase segregation necessarily introduces splay deformations of the segment orientation, we find that increasing the stiffness K raises the critical χN at the onset of microphase separation. Furthermore, we find that strong orientational interactions in one block give rise to highly asymmetric phase diagrams due to the large penalty for high-splay morphologies, such as the cylindrical phase. Finally, we analyze the costs of inter-segmental splay as well as the size dependence of domain spacing on K based on a strong-segregation picture of morphologies. [ABSTRACT FROM AUTHOR]

Raman spectroscopy and synchrotron x-ray diffraction measurements of ammonia (NH3) in laser-heated diamond anvil cells, at pressures up to 60 GPa and temperatures up to 2500 K, reveal that the melting line exhibits a maximum near 37 GPa and intermolecular proton fluctuations substantially increase in the fluid with pressure. We find that NH3 is chemically unstable at high pressures, partially dissociating into N2 and H2. Ab initio calculations performed in this work show that this process is thermodynamically driven. The chemical reactivity dramatically increases at high temperature (in the fluid phase at T > 1700 K) almost independent of pressure. Quenched from these high temperature conditions, NH3 exhibits structural differences from known solid phases. We argue that chemical reactivity of NH3 competes with the theoretically predicted dynamic dissociation and ionization. [ABSTRACT FROM AUTHOR]