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The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis. [ABSTRACT FROM AUTHOR]

The soldering reactions of three-segmented Sn/Cu/Sn and Cu/Sn/Cu diffusion couples are monitored by in-situ transmission electron microscopy to reveal the metallurgical reaction mechanism and the associated phase transformation pathway. For Sn/Cu/Sn diffusion couples, there is no ε-Cu3Sn formation due to the relatively insufficient Cu as compared to Sn. Kirkendall voids form initially in the Cu segment and then disappear due to the volume expansion associated with the continued intermetallic compound (IMC) formation as the reaction progresses. The incoming Sn atoms react with Cu to form η-Cu6Sn5, and the continuous reaction then transforms the entire nanowire to η-Cu6Sn5 grains with remaining Sn. With continued heating slightly above the melting point of Sn, an Sn-rich liquid phase forms between η-Cu6Sn5 grains. By contrast, the reaction in the Cu/Sn/Cu diffusion couples results in the intermetallic phases of both Cu3Sn and Cu6Sn5 and the development of Cu6Sn5 bulges on Cu3Sn grains. Kirkendall voids form in the two Cu segments, which grow and eventually break the nanowire into multiple segments. [ABSTRACT FROM AUTHOR]

In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the sketch community – that annotated supervisory data are difficult to obtain. This problem is very challenging in which sketches are highly abstract and subject to different drawing styles, making existing solutions tailored for photos unsuitable. Key for the success of our self-supervised learning paradigm lies with our sketch-specific designs: (i) we propose a set of pretext tasks specifically designed for sketches that mimic different drawing styles, and (ii) we further exploit the use of the textual convolution network (TCN) together with the convolutional neural network (CNN) in a dual-branch architecture for sketch feature learning, as means to accommodate the sequential stroke nature of sketches. We demonstrate the superiority of our sketch-specific designs through two sketch-related applications (retrieval and recognition) on a million-scale sketch dataset, and show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods, and significantly narrows the performance gap between with supervised representation learning. 1 PyTorch code of this work is available at https://github.com/zzz1515151/self-supervised_learning_sketch. [ABSTRACT FROM AUTHOR]

We report in situ atomic-scale transmission electron microscopy observations of the surface dynamics during Cu2O reduction. We show inhomogeneous oxide reduction caused by the preferential adsorption of hydrogen at step edges that induces oxygen loss and destabilizes Cu atoms within the step edge, thereby resulting in the retraction motion of atomic steps at the oxide surface. [ABSTRACT FROM AUTHOR]

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are insufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. [ABSTRACT FROM AUTHOR]

One dimensional tin/indium (Sn/In) nanowires have been synthesized through a surfactant assisted chemical reduction method in aqueous solutions at low temperature (∼0 °C). Synthetic parameters such as stirring speed, injection rate of reducing agent, and alloy ratio were used to control the size and aspect ratio of the resulting nanowires. It was observed that the diameter of the nanowires was around 70 nm for all the synthesis conditions that were studied, whereas the length of the nanowires can be controlled in the range of 400 nm to 2 μm. The morphology and structure of the Sn/In nanowires were characterized by field-emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HRTEM), and X-ray powder diffraction (XRD), and their thermal properties were measured by differential scanning calorimetry (DSC). Structure analysis indicated that the Sn/In nanowires were mainly composed of InSn 4 and Sn; whereas no In 3 Sn phase was observed in the nanowires. DSC results confirmed the structures of the Sn/In nanowires that were measured through XRD and indicated that the nanowires started to melt at a relatively low temperature around 118 °C. Finally, a growth mechanism based on the reaction and structural evolution was proposed to explain the nanowire formation and growth process. [ABSTRACT FROM AUTHOR]

The volumetric energy density of today's lithium-ion batteries is limited mostly by the graphitic carbon anode. Silicon is a promising replacement but its excessive volume expansion on lithiation limits its long-term cyclability performance. A nano-sized aluminium containing silicon, leached in acid, with a porous structure is shown to maintain its capacity higher than pure bulk silicon or nano-sized silicon by over 700 mAh/g. The capacity of leached silicon is maintained at 1400 mAh/g for more than 60 cycles. X-ray diffraction, scanning electron microscopy, transmission electron microscopy and nuclear magnetic resonance spectroscopy have been used to correlate the electrochemical performance with the materials' morphology and composition. [ABSTRACT FROM AUTHOR]

Highlights: [•] Reduction of CuO nanowires results in the formation of a CuO/Cu2O hybrid structure. [•] The surface of CuO nanowires is decorated by embedded Cu2O particles. [•] Reduction of CuO nanowires occurs via nucleation and growth of Cu2O particles. [ABSTRACT FROM AUTHOR]

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot. [ABSTRACT FROM AUTHOR]

The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, F 1 score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals. [ABSTRACT FROM AUTHOR]

AbstractThe peroxidase-like properties of a graphene oxide/platinum-cobalt nanocomposite and the rapid color development mechanism of 3,3′,5,5′-tetramethylbenzidine (TMB) through the decomposition of ·OH from H2O2 were utilized to prepare a nonlabelled, simple, and sensitive paper-based colorimetric sensor. This sensor allows visualization of the results and instantaneous detection, enabling quantitative measurement of H2O2. The graphene oxide/platinum cobalt composite was synthesized using a two-step procedure. Its properties were investigated using transmission electron microscopy (TEM), Raman spectroscopy, and energy-dispersive X-ray spectroscopy (EDS). The composite was subsequently transferred onto a paper substrate to create the colorimetric sensor. The optimal catalytic conditions were a composite concentration of 201.17 µg/mL, a color development time of 3 min, a TMB concentration of 2 mmol·L−1, and a pH of 4. Using the optimal conditions, the paper-based colorimetric sensor has a linear range for H2O2 from 1.0 × 10−5 to 0.1 mol·L−1, with a limit of detection of 1.0 × 10−6 mol·L−1 which is comparable or better than comparable methods. This paper-based colorimetric sensor has potential applications for the rapid determination of hydrogen peroxide. [ABSTRACT FROM AUTHOR]

Motivated by government policies and decision-makers’ opportunity perception, many firms have invested in green product innovation. The effect of this type of innovation has been analyzed in extant literature thoroughly, but how to improve innovator’s economic performance is still under-researched. When considering this research gap, the business model theory is used in this paper. An empirically based and testable framework is developed. Using survey data from 231 traditional manufacturing firms in China, these hypotheses are tested. The results show that novelty-centered and efficiency-centered business model design themes all play a moderating effect on the relationship between green product innovation and firm performance, while a fit between green product innovation and novelty-centered design theme is better for firm performance. [ABSTRACT FROM AUTHOR]

Abstract Compared with the abundant achievements of technological innovation, management innovation is still under-researched in academia. Putting management innovation in the context of environmental issues, this paper seeks to answer two key questions: (1) what factors stimulate green management innovation and (2) how can green management innovation affect firm performance. On the basis of literature review an operational definition of green management innovation is given. Then the relationships between green management innovation, its antecedents and consequences are hypothesized. A total of 267 valid questionnaires from mining industry in China are used. Different from the market pull and technology push of technological innovation, green management innovation is mainly pushed by external knowledge supply, while the internal efficiency demand does not play a significant role in our findings. Moreover, green management innovation has a positive effect on firm economic performance and internal efficiency demand moderates the effect. Our findings provide complementary insights to green management innovation. [ABSTRACT FROM AUTHOR]

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.

Sn-based alloy materials are strong candidates to replace graphitic carbon as the anode for the next generation lithium-ion batteries because of their much higher gravimetric and volumetric capacity. A series of nanosize Sn y Fe alloys derived from the chemical transformation of preformed Sn nanoparticles as templates have been synthesized and characterized. An optimized Sn 5 Fe/Sn 2 Fe anode with a core-shell structure delivered 541 mAh·g -1 after 200 cycles at the C/2 rate, retaining close to 100% of the initial capacity. Its volumetric capacity is double that of commercial graphitic carbon. It also has an excellent rate performance, delivering 94.8, 84.3, 72.1, and 58.2% of the 0.1 C capacity (679.8 mAh/g) at 0.2, 0.5, 1 and 2 C, respectively. The capacity is recovered upon lowering the rate. The exceptional cycling/rate capability and higher gravimetric/volumetric capacity make the Sn y Fe alloy a potential candidate as the anode in lithium-ion batteries. The understanding of Sn y Fe alloys from this work also provides insight for designing other Sn-M (M = Co, Ni, Cu, Mn, etc.) system., Competing Interests: The authors declare no competing financial interest.

ε-LiVOPO 4 is a promising multielectron cathode material for Li-ion batteries that can accommodate two electrons per vanadium, leading to higher energy densities. However, poor electronic conductivity and low lithium ion diffusivity currently result in low rate capability and poor cycle life. To enhance the electrochemical performance of ε-LiVOPO 4 , in this work, we optimized its solid-state synthesis route using in situ synchrotron X-ray diffraction and applied a combination of high-energy ball-milling with electronically and ionically conductive coatings aiming to improve bulk and surface Li diffusion. We show that high-energy ball-milling, while reducing the particle size also introduces structural disorder, as evidenced by 7 Li and 31 P NMR and X-ray absorption spectroscopy. We also show that a combination of electronically and ionically conductive coatings helps to utilize close to theoretical capacity for ε-LiVOPO 4 at C/50 (1 C = 153 mA h g -1 ) and to enhance rate performance and capacity retention. The optimized ε-LiVOPO 4 /Li 3 VO 4 /acetylene black composite yields the high cycling capacity of 250 mA h g -1 at C/5 for over 70 cycles., Competing Interests: The authors declare no competing financial interest.

Surface segregation-the enrichment of one element at the surface, relative to the bulk-is ubiquitous to multi-component materials. Using the example of a Cu-Au solid solution, we demonstrate that compositional variations induced by surface segregation are accompanied by misfit strain and the formation of dislocations in the subsurface region via a surface diffusion and trapping process. The resulting chemically ordered surface regions acts as an effective barrier that inhibits subsequent dislocation annihilation at free surfaces. Using dynamic, atomic-scale resolution electron microscopy observations and theory modelling, we show that the dislocations are highly active, and we delineate the specific atomic-scale mechanisms associated with their nucleation, glide, climb, and annihilation at elevated temperatures. These observations provide mechanistic detail of how dislocations nucleate and migrate at heterointerfaces in dissimilar-material systems.

The atomic-scale reduction mechanism of α-Fe 2 O 3 nanowires by H 2 was followed using transmission electron microscopy to reveal the evolution of atomic structures and the associated transformation pathways for different iron oxides. The reduction commences with the generation of oxygen vacancies that order onto every 10 th [Formula: see text] plane. This vacancy ordering is followed by an allotropic transformation of α-Fe 2 O 3 → γ-Fe 2 O 3 along with the formation of Fe 3 O 4 nanoparticles on the surface of the γ-Fe 2 O 3 nanowire by a topotactic transformation process, which shows 3D correspondence between the structures of the product and its host. These observations demonstrate that the partial reduction of α-Fe 2 O 3 nanowires results in the formation of a unique hierarchical structure of hybrid oxides consisting of the parent oxide phase, γ-Fe 2 O 3 , as the one-dimensional wire and the Fe 3 O 4 in the form of nanoparticles decorated on the parent oxide skeleton. We show that the proposed mechanism is consistent with previously published and our density functional theory results on the thermodynamics of surface termination and oxygen vacancy formation in α-Fe 2 O 3 . Compared to previous reports of α-Fe 2 O 3 directly transformed to Fe 3 O 4 , our work provides a more in-depth understanding with substeps of reduction, i.e., the whole reduction process follows: α-Fe 2 O 3 → α-Fe 2 O 3 superlattice → γ-Fe 2 O 3 + Fe 3 O 4 → Fe 3 O 4 .

Olivine MnPO4 is the delithiated phase of the lithium-ion-battery cathode (positive electrode) material LiMnPO4, which is formed at the end of charge. This phase is metastable under ambient conditions and can only be produced by delithiation of LiMnPO4. We have revealed the manganese dissolution phenomenon during chemical delithiation of LiMnPO4, which causes amorphization of olivine MnPO4. The properties of crystalline MnPO4 obtained from carbon-coated LiMnPO4 and of the amorphous product resulting from delithiation of pure LiMnPO4 were studied and compared. The phosphorus-rich amorphous phases in the latter are considered to be MnHP2O7 and MnH2P2O7 from NMR, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy analysis. The thermal stability of MnPO4 is significantly higher under high vacuum than at ambient condition, which is shown to be related to surface water removal.