Carbon Nanofiber-Based Materials as Anode Materials for Lithium-Ion Batteries

Considerable efforts have been devoted to the research of high-performance and long-lifespan lithium-ion batteries (LIBs) for their applications in large-scale power units. As one of the most important components in LIBs, anode plays an important role in determining the overall performance of LIBs. Nowadays, graphite has been the most successfully commercialized anode material. However, its limited theoretical capacity (372 mA h g−1) and limited power density seems insufficient for the next-generation LIBs. To overcome these problems, new materials with fundamentally higher capacity and higher power density are urgently needed. Recently, there is an ever-increasing interest in developing novel carbonaceous nanomaterials to replace graphite as the anode materials for LIBs. Such materials have included carbon spheres, carbon nanotubes, carbon nanofibers (CNFs), porous monoliths, and graphene. Among these alternative forms of carbon, CNFs and its morphological-controlled derivatives (such as porous or hollow CNFs) have attracted much attention due to their unique and interesting properties such as one-dimensional (1D) nanostructure, good electronic conductivity, and large surface areas. Moreover, these CNFs can be used to encapsulate various second phases to form some functional composite, meeting further requirements including higher energy density, higher power density or flexible requirements, for the advanced LIB operation

Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis

Energy minimization with one dot fuzzy initialization for marine oil spill segmentation

Detecting marine oil spill regions in synthetic aperture radar (SAR) images has always been posed as a segmentation problem in terms of minimizing a certain energy function(al). As most energy minimization problems do not have analytical solutions, minimizing an energy function(al) is usually achieved in an iterative numerical manner. In this scenario, one key factor that affects the segmentation accuracy is the initialization for starting or constraining the numerical iterations. To guarantee accurate segmentation, a proper initialization that characterizes the marine oil spill layouts in a SAR image is required. However, marine oil spill regions are always complicatedly shaped, and it is inefficient to manually devise precise initializations for capturing various marine oil spill shapes. In order to address this problem and render efficient and robust segmentation, we develop a one dot fuzzy initialization strategy. In contrast to the normal practice of manually labeling a large amount of pixels (possibly lines or cycles of pixels subject to strict spatial conditions) as initialization, our strategy just requires one arbitrary pixel within a marine oil spill region as the initial dot. The intuition of our strategy is that the fuzzy connectedness between an arbitrary initial dot and the rest pixels enables the derivation of a physically homogeneous region which is consistent for initializing the energy minimization. In the light of this observation, we develop schemes for exploiting the one dot derived region to initialize both level sets for minimizing continuous energy functionals and graph cuts for minimizing discrete energy functions. Experimental results validate the robustness of our one dot fuzzy initialization strategy

Level sets with one dot fuzzy initialization for marine oil spill segmentation

Image segmentation techniques are widely used for identifying marine oil spill regions in SAR images, with one representative technique being level set evolution. The accuracy of level set evolution highly relies on a proper initial level set function which roughly captures the marine oil spill layouts in a SAR image. However, marine oil spill regions are always complicatedly shaped, and it is time-consuming and impractical to manually devise precise initial level set functions for various marine oil spill shapes. In order to address this problem and achieve efficient and robust segmentation, we develop a one dot initialization scheme for level set evolution. Our method just requires an arbitrary pixel within one marine oil spill region as initialization. It exploits fuzzy connectedness between pixels such that consistent initial level set functions can be established via arbitrary initial dots within the marine oil spill region. Experimental results validates the robustness of our strategy

Live cell imaging of DNA and RNA with fluorescent signal amplification and background reduction techniques

Illuminating DNA and RNA dynamics in live cell can elucidate their life cycle and related biochemical activities. Various protocols have been developed for labeling the regions of interest in DNA and RNA molecules with different types of fluorescent probes. For example, CRISPR-based techniques have been extensively used for imaging genomic loci. However, some DNA and RNA molecules can still be difficult to tag and observe dynamically, such as genomic loci in non-repetitive regions. In this review, we will discuss the toolbox of techniques and methodologies that have been developed for imaging DNA and RNA. We will also introduce optimized systems that provide enhanced signal intensity or low background fluorescence for those difficult-to-tag molecules. These strategies can provide new insights for researchers when designing and using techniques to visualize DNA or RNA molecules

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

Aptamer nucleotide analog drug conjugates in the targeting therapy of cancers

Aptamers are short single-strand oligonucleotides that can form secondary and tertiary structures, fitting targets with high affinity and specificity. They are so-called “chemical antibodies” and can target specific biomarkers in both diagnostic and therapeutic applications. Systematic evolution of ligands by exponential enrichment (SELEX) is usually used for the enrichment and selection of aptamers, and the targets could be metal ions, small molecules, nucleotides, proteins, cells, or even tissues or organs. Due to the high specificity and distinctive binding affinity of aptamers, aptamer–drug conjugates (ApDCs) have demonstrated their potential role in drug delivery for cancer-targeting therapies. Compared with antibodies which are produced by a cell-based bioreactor, aptamers are chemically synthesized molecules that can be easily conjugated to drugs and modified; however, the conventional ApDCs conjugate the aptamer with an active drug using a linker which may add more concerns to the stability of the ApDC, the drug-releasing efficiency, and the drug-loading capacity. The function of aptamer in conventional ApDC is just as a targeting moiety which could not fully perform the advantages of aptamers. To address these drawbacks, scientists have started using active nucleotide analogs as the cargoes of ApDCs, such as clofarabine, ara-guanosine, gemcitabine, and floxuridine, to replace all or part of the natural nucleotides in aptamer sequences. In turn, these new types of ApDCs, aptamer nucleotide analog drug conjugates, show the strength for targeting efficacy but avoid the complex drug linker designation and improve the synthetic efficiency. More importantly, these classic nucleotide analog drugs have been used for many years, and aptamer nucleotide analog drug conjugates would not increase any unknown druggability risk but improve the target tumor accumulation. In this review, we mainly summarized aptamer-conjugated nucleotide analog drugs in cancer-targeting therapies