•The system and process level investigations of wire sawing technology, including both multi-wire slurry sawing and diamond wire sawing, are summarized.•Ingot materials used in wire sawing ...technology, as well as their properties and behavior during sawing operation are discussed.•A review of models of single grit indentation and scribing of brittle materials, as well modified models proposed particularly for wire sawing process, is presented.•Important research aspects to be further worked on to gain more complete scientific understanding of wire sawing technology are also discussed.
Wire sawing technology has been widely adopted for slicing of brittle-and-hard materials including crystalline silicon, SiC and sapphire. This paper presents a literature review on the research efforts on wire sawing related topics. First, the system and process level investigations of wire sawing technology, including both multi-wire slurry sawing and diamond wire sawing, are summarized. Ingot materials used in wire sawing technology, as well as their properties and behavior during sawing operation are discussed. As modeling and analysis of single grit indentation and scribing of brittle materials provide fundamental insight on material removal and these can be leveraged for wire sawing analysis at system level, a review of those models and modified models proposed particularly for wire sawing process are also presented. After the survey of current state-of-the-art, this contribution proposes important research aspects to be further worked on to gain more complete scientific understanding of wire sawing technology.
Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for hyperspectral image classification tasks. Training deep neural networks, ...such as a convolutional neural network for classification requires a large number of labeled samples. However, in remote sensing applications, we usually only have a small amount of labeled data for training because they are expensive to collect, although we still have abundant unlabeled data. In this paper, we propose semi-supervised deep learning for hyperspectral image classification-our approach uses limited labeled data and abundant unlabeled data to train a deep neural network. More specifically, we use deep convolutional recurrent neural networks (CRNN) for hyperspectral image classification by treating each hyperspectral pixel as a spectral sequence. In the proposed semi-supervised learning framework, the abundant unlabeled data are utilized with their pseudo labels (cluster labels). We propose to use all the training data together with their pseudo labels to pre-train a deep CRNN, and then fine-tune using the limited available labeled data. Further, to utilize spatial information in the hyperspectral images, we propose a constrained Dirichlet process mixture model (C-DPMM), a non-parametric Bayesian clustering algorithm, for semi-supervised clustering which includes pairwise must-link and cannot-link constraints-this produces high-quality pseudo-labels, resulting in improved initialization of the deep neural network. We also derived a variational inference model for the C-DPMM for efficient inference. Experimental results with real hyperspectral image data sets demonstrate that the proposed semi-supervised method outperforms state-of-the-art supervised and semi-supervised learning methods for hyperspectral classification.
In this paper, we systematically proposed the strategy of tailoring strain delocalization to evade long-standing strength-ductility trade-off dilemma. The scientific contribution is to define and, ...for the first time, to expand the category of strain localization into the whole deformation process, including elastic lattice distortion, plasticity-relevant statistical behaviors (dislocation, twinning, shear/slip bands, necking, etc.), and crack-dependent damage accumulation. The viewpoint we proposed is that the achieving of strength-ductility synergy depends on the delocalizing of aforementioned localized strains. Using hierarchical materials as an example, the design of heterogeneous structure significantly influences the strain delocalization behaviors in terms of internal stress/strain (elastic stage), local strain evolution (plastic stage), and cracking (fracture stage). Relationships among the heterogeneous microstructure, microscopic stress/strain evolution, macroscopic mechanical properties are established. In particular, we assess their influences on strain delocalization from the perspective of slip transfer, plastic stability, damage micromechanics, and crack propagation. A methodological framework is then suggested to understand the materials behaviors in the future using the rapidly developed physics-based multi-dimensional computational models and advanced in situ strain characterization techniques. Innovations towards excellent strength-ductility synergy and expanding applications are increasingly advocated, through promoting strain delocalization and indentifying the current challenges and future opportunities.
•A deep convolutional neural network model based fault diagnosis method is proposed for chemical processes.•A deep convolutional neural network model is constructed and applied in the Tennessee ...Eastman process.•An average fault diagnosis rate of 88.2% is achieved.•The model tuning and the dynamic diagnostic performance are explored.
Numerous accidents in chemical processes have caused emergency shutdowns, property losses, casualties and/or environmental disruptions in the chemical process industry. Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences. However, FDD is still far from widely practical applications. Over the past few years, deep convolutional neural network (DCNN) has shown excellent performance on machine-learning tasks. In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis. The benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance of the fault diagnosis method.
As one of the most critical approaches to resolve the energy crisis and environmental concerns, carbon dioxide (CO2) photoreduction into value‐added chemicals and solar fuels (for example, CO, HCOOH, ...CH3OH, CH4) has attracted more and more attention. In nature, photosynthetic organisms effectively convert CO2 and H2O to carbohydrates and oxygen (O2) using sunlight, which has inspired the development of low‐cost, stable, and effective artificial photocatalysts for CO2 photoreduction. Due to their low cost, facile synthesis, excellent light harvesting, multiple exciton generation, feasible charge‐carrier regulation, and abundant surface sites, semiconductor quantum dots (QDs) have recently been identified as one of the most promising materials for establishing highly efficient artificial photosystems. Recent advances in CO2 photoreduction using semiconductor QDs are highlighted. First, the unique photophysical and structural properties of semiconductor QDs, which enable their versatile applications in solar energy conversion, are analyzed. Recent applications of QDs in photocatalytic CO2 reduction are then introduced in three categories: binary II–VI semiconductor QDs (e.g., CdSe, CdS, and ZnSe), ternary I–III–VI semiconductor QDs (e.g., CuInS2 and CuAlS2), and perovskite‐type QDs (e.g., CsPbBr3, CH3NH3PbBr3, and Cs2AgBiBr6). Finally, the challenges and prospects in solar CO2 reduction with QDs in the future are discussed.
Carbon dioxide (CO2) photoreduction is regarded as an attractive pathway to produce value‐added chemicals and fuels. Recent advances in CO2 photoreduction via semiconductor quantum dots (QDs) in three categories are reviewed: II–VI, I–III–VI, and perovskite‐type QDs. Additionally, current challenges and prospects for QD‐photocatalyzed CO2 reduction are discussed.
Methylation of cytosines in the mammalian genome represents a key epigenetic modification and is dynamically regulated during development. Compelling evidence now suggests that dynamic regulation of ...DNA methylation is mainly achieved through a cyclic enzymatic cascade comprised of cytosine methylation, iterative oxidation of methyl group by TET dioxygenases, and restoration of unmodified cytosines by either replication-dependent dilution or DNA glycosylase-initiated base excision repair. In this review, we discuss the mechanism and function of DNA demethylation in mammalian genomes, focusing particularly on how developmental modulation of the cytosine-modifying pathway is coupled to active reversal of DNA methylation in diverse biological processes.
A critical and challenging process in immunotherapy is to identify cancer patients who could benefit from immune checkpoint inhibitors (ICIs). Exploration of predictive biomarkers could help to ...maximize the clinical benefits. Eph receptors have been shown to play essential roles in tumor immunity. However, the association between EPH gene mutation and ICI response is lacking.
Clinical data and whole-exome sequencing (WES) data from published studies were collected and consolidated as a discovery cohort to analyze the association between EPH gene mutation and efficacy of ICI therapy. Another independent cohort from Memorial Sloan Kettering Cancer Center (MSKCC) was adopted to validate our findings. The Cancer Genome Atlas (TCGA) cohort was used to perform anti-tumor immunity and pathway enrichment analysis.
Among fourteen EPH genes, EPHA7-mutant (EPHA7-MUT) was enriched in patients responding to ICI therapy (FDR adjusted P < 0.05). In the discovery cohort (n = 386), significant differences were detected between EPHA7-MUT and EPHA7-wildtype (EPHA7-WT) patients regarding objective response rate (ORR, 52.6% vs 29.1%, FDR adjusted P = 0.0357) and durable clinical benefit (DCB, 70.3% vs 42.7%, FDR adjusted P = 0.0200). In the validation cohort (n = 1144), significant overall survival advantage was observed in EPHA7-MUT patients (HR = 0.62 95% confidence interval, 0.39 to 0.97, multivariable adjusted P = 0.0367), which was independent of tumor mutational burden (TMB) and copy number alteration (CNA). Notably, EPHA7-MUT patients without ICI therapy had significantly worse overall survival in TCGA cohort (HR = 1.33 95% confidence interval, 1.06 to 1.67, multivariable adjusted P = 0.0139). Further gene set enrichment analysis revealed enhanced anti-tumor immunity in EPHA7-MUT tumor.
EPHA7-MUT successfully predicted better clinical outcomes in ICI-treated patients across multiple cancer types, indicating that EPHA7-MUT could serve as a potential predictive biomarker for immune checkpoint inhibitors.
We here attempt to achieve an integrated understanding of the structure and dynamics of a number of higher-order assemblies, including amyloids, various kinds of signalosomes, and cellular granules. ...We propose that the synergy between folded domains, linear motifs, and intrinsically disordered regions regulates the formation and intrinsic fuzziness of all higher-order assemblies, creating a structural and dynamic continuum. We describe how such regulatory mechanisms could be influenced under pathological conditions.
The synergy between folded domains, linear motifs, and intrinsically disordered regions creates a structural and dynamic continuum of higher-order assemblies.
DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile ...DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods.
We present a novel statistical model to detect differentially methylated loci from BS-seq data under general experimental design, based on a beta-binomial regression model with 'arcsine' link function. Parameter estimation is based on transformed data with generalized least square approach without relying on iterative algorithm. Simulation and real data analyses demonstrate that our method is accurate, powerful, robust and computationally efficient.
It is available as Bioconductor package DSS.
yongpark@pitt.edu or hao.wu@emory.edu
Supplementary data are available at Bioinformatics online.
Conventional liquid electrolytes for lithium batteries usually suffer from irreversible decomposition and safety concerns. Solid state electrolytes (SSEs) have been considered as the key for advanced ...lithium batteries with improved energy density and safety, whereas challenges remain for polymer and inorganic SSEs. Recently, hybrid solid‐state electrolytes (HSSEs) that integrate the merits of different electrolyte systems have been under intensive study. Herein, we summarize the recent progress of HSSEs with different compositions and structures. The design principle of each type of HSSEs are discussed, as well as their ionic conducting mechanism, electrochemical performance and effects of compositional/structural control. Finally, challenges and perspectives are provided for the future development of HSSEs and solid‐state lithium batteries.
Hybrid solid‐state electrolytes (HSSEs) are keys to the development of lithium batteries with enhanced energy density and safety. This Minireview summarizes the recent development of HSSEs and discusses their design principles, performance and ionic conducting mechanism.
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