Understanding and Mitigating Lattice Collapse Degradation in Layered Oxide Materials for Sodium-Ion Battery Anode.

Na2Ti3O7 has attracted significant attention due to its ecofriendliness and cost-effectiveness for sodium-ion batteries. However, their limited cycling stability hampers their practical applications. Herein, we elucidate a mechanism of structural degradation caused by the heterogeneous phase transition in the Na2Ti3O7 anode using aberration-corrected (scanning) transmission electron microscopy (S)TEM and in situ TEM. It is found that the unevenly distributed phase transition results in the accumulation of strain, which promotes the growth of microcracks and eventually leads to structural decomposition and electrochemical failure. Motivated by this degradation mechanism, nanowires were proposed, and the structural stability is thus improved with the lattice strain effectively released. These findings deepen our understanding of ion transport and degradation mechanisms in intercalated layered electrode materials while emphasizing the significance of the material structure engineered for improving electrode performance.

Metal Organic Frameworks as Polysulfide Reaction Modulators for Lithium Sulfur Batteries: Advances and Perspectives.

Currently, lithium sulfur (Li-S) battery with high theoretical energy density has attracted great research interest. However, the diffusion and loss process of intermediate lithium polysulfide during charge-discharge hindered the application of the Li-S battery in modern life. To overcome this issue, metal organic frameworks (MOFs) and their composites have been regarded as effective additions to restrain the LiPS diffusion process for Li-S battery. Benefiting from the unique structure with rich active sites to adsorb LiPS and accelerate the LiPS redox, the Li-S batteries with MOFs modified exhibit superior electrochemical performance. Considering the rapid development of MOFs in Li-S battery, this review summarizes the recent studies of MOFs and their composites as the sulfur host materials, functional interlayer, separator coating layer, and separator/solid electrolyte for Li-S batteries in detail. In addition, the promising design strategies of functional MOF materials are proposed to improve the electrochemical performance of Li-S battery.

Cluster-Aware Grid Layout.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

Anisotropic Amorphization and Phase Transition in Na2 Ti3 O7 Anode Caused by Electron Beam Irradiation.

Na2 Ti3 O7 is considered one of the most promising anode materials for sodium ion batteries due to its superior safety, environmental friendliness, and low manufacturing cost. However, its structural stability and reaction mechanism still have not been fully explored. As the electron beam irradiation introduces a similar impact on the Na2 Ti3 O7 anode as the extraction of Na+ ions during the battery discharge process, the microstructure evolution of the materials is investigated by advanced electron microscopy techniques at the atomic scale. Anisotropic amorphization is successfully observed. Through the integrated differential phase contrast-scanning transmission electron microscopy technique and density functional theory calculation, a phase transition pathway involving a new phase, Na2 Ti24 O49 , is proposed with the reduction of Na atoms. Additionally, it is found that the amorphization is dominated by the surface energy and electron dose rate. These findings will deepen the understanding of structural stability and deintercalation mechanism of the Na2 Ti3 O7 anode, providing new insight into exploring the failure mechanism of electrode materials.

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization.

Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN-Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.

Intrabasal Plane Defect Formation in NiFe Layered Double Hydroxides Enabling Efficient Electrochemical Water Oxidation.

Defect engineering has proven to be one of the most effective approaches for the design of high-performance electrocatalysts. Current methods to create defects typically follow a top-down strategy, cutting down the pristine materials into fragmented pieces with surface defects yet also heavily destroying the framework of materials that imposes restrictions on the further improvements in catalytic activity. Herein, we describe a bottom-up strategy to prepare free-standing NiFe layered double hydroxide (LDH) nanoplatelets with abundant internal defects by controlling their growth behavior in acidic conditions. Our best-performing nanoplatelets exhibited the lowest overpotential of 241 mV and the lowest Tafel slope of 43 mV/dec for the oxygen evolution reaction (OER) process, superior to the pristine LDHs and other reference cation-defective LDHs obtained by traditional etching methods. Using both material characterization and density functional theory (DFT) simulation has enabled us to develop relationships between the structure and electrochemical properties of these catalysts, suggesting that the enhanced electrocatalytic activity of nanoplatelets mainly results from their defect-abundant structure and stable layered framework with enhanced exposure of the (001) surface.

Deep Neural Network-Based Fusion Localization Using Smartphones.

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy.

This paper presents a novel method for the dynamic positioning of an unmanned underwater vehicle (UUV) with unknown trajectories based on an autonomous tracking buoy (PUVV-ATB) that indirectly positions the UUV using ultra-short baseline measurements. The method employs a spatial location geometric model and divides the positioning process into four steps, including data preprocessing to detect geometric errors and apply mean filtering, direction capture, position tracking, and position synchronization. To achieve these steps, a new adaptive tracking control algorithm is proposed that does not require trajectory prediction and is applied to the last three steps. The algorithm is deployed to the buoy for tracking simulation and sea trial experiments, and the results are compared with those of a model predictive control algorithm. The autonomous tracking buoy based on the adaptive tracking control algorithm runs more stably and can better complete the precise tracking task for the UUV with a positioning error of less than 10 cm. This method breaks the premise of trajectory prediction based on traditional tracking control algorithms, providing a new direction for further research on UUV localization. Furthermore, the conclusion of this paper has important reference value for other research and application fields related to UUV.

Searching by parts: Towards fine-grained image retrieval respecting species correlation.

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes.

A Low-Cost and Efficient Indoor Fusion Localization Method.

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Autotrophic Fe-Driven Biological Nitrogen Removal Technologies for Sustainable Wastewater Treatment.

Fe-driven biological nitrogen removal (FeBNR) has become one of the main technologies in water pollution remediation due to its economy, safety and mild reaction conditions. This paper systematically summarizes abiotic and biotic reactions in the Fe and N cycles, including nitrate/nitrite-dependent anaerobic Fe(II) oxidation (NDAFO) and anaerobic ammonium oxidation coupled with Fe(III) reduction (Feammox). The biodiversity of iron-oxidizing microorganisms for nitrate/nitrite reduction and iron-reducing microorganisms for ammonium oxidation are reviewed. The effects of environmental factors, e.g., pH, redox potential, Fe species, extracellular electron shuttles and natural organic matter, on the FeBNR reaction rate are analyzed. Current application advances in natural and artificial wastewater treatment are introduced with some typical experimental and application cases. Autotrophic FeBNR can treat low-C/N wastewater and greatly benefit the sustainable development of environmentally friendly biotechnologies for advanced nitrogen control.

High-precision atomic-scale strain mapping of nanoparticles from STEM images.

Strain is a crucial factor that influences the physicochemical properties of nanoparticles. Being able to precisely measure strain is important in understanding the intrinsic mechanism of the enhanced performance of nanoparticles. Techniques that have been developed for strain analysis using scanning transmission electron microscopy (STEM) images can be categorized into diffraction-based method and imaging-based method. Here, using image simulation techniques, it is found that the measured two-dimensional (2D) displacements from annular dark field (ADF) STEM images of a nanoparticle are a good approximation to a projection of the actual three-dimensional (3D) displacements. A methodology for deformation analysis is presented which is based on the detection of atomic columns from atomic-resolution STEM images in real space. Elastic deformation parameters such as strain are usually defined on the basis of a continuum of deformation. The appropriateness of various deformation parameters for atomic-scale investigation on STEM images is explored and a method for determining these is presented. We found that the local lattice parameter and principal strain components are the most physically meaningful parameters to express the materials distortion behaviour. Apart from the local lattice parameter, the other deformation parameters such as normal strains, shear strains and displacements, heavily rely on the choice of reference lattice. It is also found that different reference grids add a series of uniform offsets to these strain variations. Finally, this approach is applied to a PtCo3 bimetallic nanoparticle to quantify its deformation behaviour.

Description and genome analysis of Luteimonas viscosa sp. nov., a novel bacterium isolated from soil of a sunflower field.

Strain XBU10T was isolated from a soil sample of a sunflower plot in Inner Mongolia, China. The isolate was a Gram-stain-negative, aerobic, non-motile, rod-shaped bacterium, and its colonies were bright yellow in colour. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain XBU10T belonged to the genus Luteimonas of the family Lysobacteraceae and was most closely related to Luteimonas panaciterrae Gsoil 068T (97.8%), Luteimonas marina FR1330T (97.6%), Luteimonas aquatica RIB1-20T (97.4%) and Luteimonas huabeiensis HB2T (97.2%). Growth occurred at 4-40 °C (optimum, 28-30 °C), with 0-5.0% (w/v) NaCl (optimum, 0.5%) and at pH 6.0-10.0 (optimum, pH 7.0 - 8.0). The chemotaxonomic characteristics of strain XBU10T, which had Q-8 as its predominant quinone and iso-C17:1 ω9c, iso-C15:0, iso-C17:0 and iso-C16:0 as its major fatty acids, were consistent with classification in the genus Luteimonas. The polar lipid profile of strain XBU10T comprised phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, one unidentified phospholipid, two unidentified aminophospholipids and three unidentified polar lipids. The genome of strain XBU10T was 4.17 Mbp with a G + C content of 69.9%. Its genome sequence showed genes encoding alkaline phosphatase and catalase. Protein-coding genes related to carbohydrate-active enzymes were also observed. Average nucleotide identity (ANI) values between XBU10T and other species of the genus Luteimonas were found to be low (ANIm < 88.0%, ANIb < 85.0% and OrthoANIu < 85.0%). Furthermore, digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) values between strain XBU10T and the closely related species ranged from 20.3 to 28.9% and from 64.2 to 82.3%, respectively. Based on the results of our phylogenetic, phenotypic, genotypic and chemotaxonomic analyses, it is concluded that strain XBU10T represents a novel species within the genus Luteimonas, for which the name Luteimonas viscosa sp. nov. is proposed. The type strain is XBU10T (= CGMCC 1.12158T = KCTC 23878T).

Bisulfite activated permanganate for oxidative water decontamination.

Recently, bisulfite-activated permanganate (MnO4-; Mn(VII)) process has attracted considerable attention as a novel class of advanced oxidation technology for destruction of organic contaminants in water. However, disputes over the underlying activation mechanism as well as reactive species generated in the Mn(VII)/bisulfite system remain for a long period due to the fairly complex chemistry involved in this system. This article aims to present a critical review on scientific development of the Mn(VII)/bisulfite system, with particular focus on the generation and contribution of various reactive intermediates. Both reactive manganese species (RMnS) (i.e., soluble Mn(III), Mn(V), and Mn(VI)) and radical species (primarily SO4•-) are identified as the oxidizing components responsible for enhanced degradation of organic contaminants by the Mn(VII)/bisulfite system. Bisulfite plays a dual role of being an activating agent for reactive intermediates generation and acting as a complexing agent to stabilize RMnS. Solution chemistry (e.g., the [Mn(VII)]/[bisulfite] molar ratio, solution pH, the type of contaminants, ligands, and water matrix components) greatly impacts the generation and consumption of RMnS and radicals, thus influencing the degradation kinetics and pathways of organics. Particularly, dissolved oxygen (DO) is a vital factor for driving the oxidation of organics since the absence of DO can block the generation of SO4•- and meantime causes the consumption of RMnS by excess SO3•- as a strong reductant. Interestingly, ferrate (FeO42-, Fe(VI)) and hexavalent chromium (CrO42-/HCrO4-, Cr(VI)) that are high-valent metal oxyanions analogous to Mn(VII) can be activated by bisulfite via a similar pathway (i.e. both high-valent metal-oxo intermediates and reactive radicals are involved). Furthermore, key knowledge gaps are identified and future research needs are proposed to address the potential challenges encountered in practical application of the Mn(VII)/bisulfite oxidation technology.

Activated carbon as an insoluble electron shuttle to enhance the anaerobic ammonium oxidation coupled with Fe(III) reduction process.

Anaerobic ammonium oxidation coupled with Fe(III) reduction (Feammox) is an autotrophic biological nitrogen removal (BNR) technique in treating low-C/N wastewater. However, the nitrogen removal rate of Feammox is limited by the extracellular electron transfer. In this study, wood activated carbon (AC) was chosen as electron shuttle to enhance the start-up of the Feammox process. Within an operational period of 150 days, the NH4+-N removal efficiency reached 97.9-99.5% with a volumetric loading rate (VLR) of 0.04-0.06 kg N m-3 d-1. Batch experiments indicated that compared with Fe2O3-AQDS and Fe2O3 groups, Fe2O3-AC group showed higher catalytic performance and TN removal efficiency reached 85.7%. Quinone (CO) and phenolic (-OH) chemical groups of AC were equipped with electron transfer capacity (76.51 ± 9.27 µmol e- g-1). Moreover, Fe(II)/Fe(III) species and the secondary iron minerals were found in our system. Microbial analysis showed that Proteobacteria and Acidobacteriota, which observed with relatively high abundance, were played an important role in the integrated Feammox system. This study demonstrates the significant influence of AC on Feammox process and provides an enhanced biological nitrogen removal strategy for practice engineering application.

Discriminative Codebook Hashing for Supervised Video Retrieval.

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback-Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.

Indoor Acoustic Signals Enhanced Algorithm and Visualization Analysis.

A pure acoustic signal can be easy to realize signal analysis and feature extraction. However, the surrounding noises will affect the content of acoustic signals as well as auditory fatigue to the audience. Therefore, it is vital to overcome the problem of noises that affect the acoustic signal. An indoor acoustic signal enhanced method based on image source (IS) method, filtered-x least mean square (FxLMS) algorithm, and the combination of Delaunay triangulation and fuzzy c-means (FCM) clustering algorithm is proposed. In the first stage of the proposed system, the IS method was used to simulate indoor impulse response. Next, the FxLMS algorithm was used to reduce the acoustic signals with noise. Lastly, the quiet areas are optimized and visualized by combining the Delaunay triangulation and FCM clustering algorithm. The experimental analysis results on the proposed system show that better noise reduction can be achieved than the most widely used least mean square algorithm. Visualization was validated with an intuitive understanding of the indoor sound field distribution and the quiet areas.

A Visual Analytics Approach for Structural Differences Among Graphs via Deep Learning.

Representing and analyzing structural differences among graphs help gain insight into the difference related patterns such as dynamic evolutions of graphs. Conventional solutions leverage representation learning techniques to encode structural information, but lack an intuitive way of studying structural semantics of graphs. In this article, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a deep-learning-based embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs. We validate the usability and effectiveness of our approach through case studies with three datasets.

Particle hydrodynamic simulation of thrombus formation using velocity decay factor.

BACKGROUND AND OBJECTIVE: Thrombus simulation plays an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, treatment planning, etc. Although a considerable number of methods have been developed to simulate various kinds of fluid flows, it remains a non-trivial task to effectively simulate thrombus because of its unique physiological properties in contrast to other types of fluids. To tackle this issue, this study introduces a novel method to model the formation mechanism of thrombus and its interaction with blood flow. METHODS: The proposed method for thrombus formation simulation mainly consists of three steps. First, we formulate the formation of thrombus as a particle-based model and obtain the fibrin concentration of the particles with a discretized form of the convection-diffusion-reaction equation; then, we calculate the velocity decay factor using the obtained fibrin concentration. Finally, the formation of thrombus can be simulated by applying the velocity decay factor on particles. RESULTS: We carried out extensive experiments under different settings to verify the efficacy of the proposed method. The experimental results demonstrate that our method can yield more realistic simulation of thrombus and is superior to peer method in terms of computational efficiency, maintaining the stability of the dynamic particle motion, and preventing particle penetration at the boundary. CONCLUSION: The proposed method can simulate the formation mechanism of thrombus and the interaction between blood flow and thrombus both efficiently and effectively.

Bubble storytelling with automated animation: a Brexit hashtag activism case study.

ABSTRACT: Hashtag data are common and easy to acquire. Thus, they are widely used in studies and visual data storytelling. For example, a recent story by China Central Television Europe depicts Brexit as a hashtag movement displayed on an animated bubble chart. However, creating such a story is usually laborious and tedious, because narrators have to switch between different tools and discuss with different collaborators. To reduce the burden, we develop a prototype system to help explore the bubbles' movement by automatically inserting animations connected to the storytelling of the video creators and the interaction of viewers to those videos. We demonstrate the usability of our method through both use cases and a semi-structured user study.