scholarly journals Technology networks: the autocatalytic origins of innovation

2018 ◽  
Vol 5 (6) ◽  
pp. 172445 ◽  
Author(s):  
Lorenzo Napolitano ◽  
Evangelos Evangelou ◽  
Emanuele Pugliese ◽  
Paolo Zeppini ◽  
Graham Room
Keyword(s):  
Positive Reinforcement ◽  
Hierarchical Classification ◽  
Source Node ◽  
Directed Network ◽  
The Core ◽  
Source Field ◽  
Patenting Activity ◽  
Positive Spillovers

We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields.

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

scholarly journals A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11204-11224 ◽  
Author(s):  
Atena Fekr ◽  
Majid Janidarmian ◽  
Katarzyna Radecka ◽  
Zeljko Zilic
Keyword(s):  
Respiration Rate ◽  
Hierarchical Classification ◽  
Rate Measurement ◽  
Medical Cloud ◽  
Respiration Rate Measurement

scholarly journals GPCRTree: online hierarchical classification of GPCR function

2008 ◽  
Vol 1 (1) ◽  
pp. 67 ◽  
Author(s):  
Matthew N Davies ◽  
Andrew Secker ◽  
Mark Halling-Brown ◽  
David S Moss ◽  
Alex A Freitas ◽  
...  
Keyword(s):  
Hierarchical Classification

scholarly journals Developing a Delphi-Based Comprehensive Core Set from the International Classification of Functioning, Disability, and Health Framework for the Rehabilitation of Patients with Burn Injuries

2021 ◽  
Vol 18 (8) ◽  
pp. 3970
Author(s):  
Yu-Ru Lin ◽  
Jr-Yi Wang ◽  
Shun-Cheng Chang ◽  
Kwang-Hwa Chang ◽  
Hung-Chou Chen ◽  
...  
Keyword(s):  
Burn Injury ◽  
Functional Limitations ◽  
Rehabilitation Program ◽  
International Classification ◽  
Burn Injuries ◽  
International Classification Of Functioning ◽  
Disability And Health ◽  
The Core ◽  
Core Set

Burn injuries cause disability and functional limitations in daily living. In a 2015 fire explosion in Taiwan, 499 young people sustained burn injuries. The construction of an effective and comprehensive rehabilitation program that enables patients to regain their previous function is imperative. The International Classification of Functioning, Disability, and Health (ICF) includes multiple dimensions that can contribute to meeting this goal. An ICF core set was developed in this study for Taiwanese patients with burns. A consensus process using three rounds of the Delphi technique was employed. A multidisciplinary team of 30 experts from various institutions was formed. The questionnaire used in this study comprised 162 ICF second-level categories relevant to burn injuries. A 5-point Likert scale was used, and participants assigned a weight to the effect of each category on daily activities after burns. The consensus among ratings was assessed using Spearman’s ρ and semi-interquartile range indices. The core set for post-acute SCI was developed from categories that attained a mean score of ≥4.0 in the third round of the Delphi exercise. The core ICF set contained 68 categories. Of these, 19 comprised the component of body functions, 5 comprised body structures, 37 comprised activities and participation, and 7 comprised environmental factors. This preliminary core set offers a comprehensive system for disability assessment and verification following burn injury. The core set provides information for effective rehabilitation strategy setting for patients with burns. Further feasibility and validation studies are required in the future.

VirusTaxo: Taxonomic classification of virus genome using multi-class hierarchical classification by k-mer enrichment

2021 ◽  
Author(s):  
Rajan Saha Raju ◽  
Abdullah Al Nahid ◽  
Preonath Shuvo ◽  
Rashedul Islam
Keyword(s):  
Genome Sequence ◽  
Rna Viruses ◽  
Hierarchical Classification ◽  
Classification Problem ◽  
Virus Genome ◽  
Taxonomic Classification ◽  
Dna Viruses ◽  
Dna And Rna ◽  
Full Length Genome

AbstractTaxonomic classification of viruses is a multi-class hierarchical classification problem, as taxonomic ranks (e.g., order, family and genus) of viruses are hierarchically structured and have multiple classes in each rank. Classification of biological sequences which are hierarchically structured with multiple classes is challenging. Here we developed a machine learning architecture, VirusTaxo, using a multi-class hierarchical classification by k-mer enrichment. VirusTaxo classifies DNA and RNA viruses to their taxonomic ranks using genome sequence. To assign taxonomic ranks, VirusTaxo extracts k-mers from genome sequence and creates bag-of-k-mers for each class in a rank. VirusTaxo uses a top-down hierarchical classification approach and accurately assigns the order, family and genus of a virus from the genome sequence. The average accuracies of VirusTaxo for DNA viruses are 99% (order), 98% (family) and 95% (genus) and for RNA viruses 97% (order), 96% (family) and 82% (genus). VirusTaxo can be used to detect taxonomy of novel viruses using full length genome or contig sequences.AvailabilityOnline version of VirusTaxo is available at https://omics-lab.com/virustaxo/.

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