An experimental approach to quantify strain transfer efficiency of fibre bragg grating sensors to host structures

This paper developed a method to evaluate the strain transfer efficiency of fibre Bragg grating sensors to host structures. Various coatings were applied to fibre Bragg grating sensors after being fabricated. They were epoxy, silane agent and polypropylene, representing different surface properties. A neat epoxy resin plate was used as the host in which the coated fibre sensors were embedded in the central layer. The tensile strain output from the FBGs was compared with that obtained from electrical strain gauges which were attached on the surface of the specimen. A calculating method based on the measured strains was developed to quantify the strain transfer function of different surface coatings. The strain transfer coefficient obtained from the proposed method provided a direct indicator to evaluate the strain transfer efficiency of different coatings used on the FBG sensors, under either short or long-term loading. The results demonstrated that the fibre sensor without any coating possessed the best strain transfer, whereas, the worst strain transfer was created by polypropylene coating. Coatings play a most influential role in strain measurements using FBG sensors

Multiple Phases of Adopting Extranet by Business Networks: A Study of Plastics Industry in Taiwan

Extranet has received growing popularity among business-to-business trading partners today. It has been used to reduce delivery lead-time and improve customer service. The inter-organization nature of Extranet, coupled with the omnipresence of Internet, create competitive advantages for a company over those who do not have Extranet linkage. This study investigates the factors affecting the adoption of Extranet in rubbers and plastics industry. The results reveal that “characteristics of end user” is the most importance factor affecting the adoption. The other significant factors include the characteristics of business itself, degree of understanding new technology, the characteristics of business network, and the support of top management

Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading

Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations

AtomSim: web-deployed atomistic dynamics simulator

AtomSim, a collection of interfaces for computational crystallography simulations, has been developed. It uses forcefield-based dynamics through physics engines such as the General Utility Lattice Program, and can be integrated into larger computational frameworks such as the Virtual Neutron Facility for processing its dynamics into scattering functions, dynamical functions etc. It is also available as a Google App Engine-hosted web-deployed interface. Examples of a quartz molecular dynamics run and a hafnium dioxide phonon calculation are presented

Parameter-Efficient Tuning with Special Token Adaptation

Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we only modify the special token representations (e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer in Transformer-based models. PASTA achieves comparable performance to fine-tuning in natural language understanding tasks including text classification and NER with up to only 0.029% of total parameters trained. Our work not only provides a simple yet effective way of parameter-efficient tuning, which has a wide range of practical applications when deploying finetuned models for multiple tasks, but also demonstrates the pivotal role of special tokens in pretrained language models

Physiological, behavioral and subjective sadness reactivity in frontotemporal dementia subtypes.

Frontotemporal dementia (FTD), a neurodegenerative disease broadly characterized by socioemotional impairments, includes three clinical subtypes: behavioral variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA) and non-fluent variant primary progressive aphasia (nfvPPA). Emerging evidence has shown emotional reactivity impairments in bvFTD and svPPA, whereas emotional reactivity in nfvPPA is far less studied. In 105 patients with FTD (49 bvFTD, 31 svPPA and 25 nfvPPA) and 27 healthy controls, we examined three aspects of emotional reactivity (physiology, facial behavior and subjective experience) in response to a sad film. In a subset of the sample, we also examined the neural correlates of diminished aspects of reactivity using voxel-based morphometry. Results indicated that all three subtypes of FTD showed diminished physiological responding in respiration rate and diastolic blood pressure; patients with bvFTD and svPPA also showed diminished subjective experience, and no subtypes showed diminished facial behavior. Moreover, there were differences among the clinical subtypes in brain regions where smaller volumes were associated with diminished sadness reactivity. These results show that emotion impairments extend to sadness reactivity in FTD and underscore the importance of considering different aspects of sadness reactivity in multiple clinical subtypes for characterizing emotional deficits and associated neurodegeneration in FTD