scholarly journals The distribution of benthic amphipod crustaceans in Indonesian seas

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12054
Author(s):  
Tri Arfianti ◽  
Mark John Costello
Keyword(s):  
Neural Network ◽  
Sampling Methods ◽  
Fish Predation ◽  
Future Research ◽  
Marine Biodiversity ◽  
Coral Triangle ◽  
Research Needs ◽  
Indonesian Seas ◽  
Biogeographic Region ◽  
Network Method

Amphipod crustaceans are an essential component of tropical marine biodiversity. However, their distribution and biogeography have not been analysed in one of the world’s largest tropical countries nested in the Coral Triangle, Indonesia. We collected and identified amphipod crustaceans from eight sites in Indonesian waters and combined the results with data from 32 additional sites in the literature. We analysed the geographic distribution of 147 benthic amphipod crustaceans using cluster analysis and the ‘Bioregions Infomaps’ neural network method of biogeographic discrimination. We found five groups of benthic amphipod crustaceans which show relationships with sampling methods, depth, and substrata. Neural network biogeographic analysis indicated there was only one biogeographic region that matched with the global amphipod regions and marine biogeographic realms defined for all marine taxa. There was no support for Wallaces or other lines being marine biogeographic boundaries in the region. Species richness was lower than expected considering the region is within the Coral Triangle. We hypothesise that this low richness might be due to the intense fish predation which may have limited amphipod diversification. The results indicated that habitat rather than biogeography determines amphipod distribution in Indonesia. Therefore, future research needs to sample more habitats, and consider habitat in conservation planning.

scholarly journals Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality Based on Structural Connectivity

2021 ◽  
Author(s):  
Josh Neudorf ◽  
Shaylyn Kress ◽  
Ron Borowsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Functional Connectivity ◽  
Brain Research ◽  
Structural Connectivity ◽  
Model Performance ◽  
Future Research ◽  
Ideal Model ◽  
Network Method ◽  
Deep Learning Model

AbstractAlthough functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. The most successful recent whole-brain methods have managed to account for 36% of the variance in functional connectivity based on structural connectivity. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 81% of the variance in functional connectivity, more than double that of the previous best model, and 99% of the variance in functional centrality. Regions of particular importance to the model’s performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

Understanding Limited Use of Amplification in Infants and Children Who Are Hard of Hearing

2015 ◽  
Vol 25 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Ryan W. McCreery ◽  
Elizabeth A. Walker ◽  
Meredith Spratford
Keyword(s):  
Hard Of Hearing ◽  
Hearing Aid ◽  
Management Strategies ◽  
Infants And Children ◽  
Future Research ◽  
Research Needs ◽  
Case Examples ◽  
Hearing Aid Use ◽  
Wide Range ◽  
In Infants And Children

The effectiveness of amplification for infants and children can be mediated by how much the child uses the device. Existing research suggests that establishing hearing aid use can be challenging. A wide range of factors can influence hearing aid use in children, including the child's age, degree of hearing loss, and socioeconomic status. Audiological interventions, including using validated prescriptive approaches and verification, performing on-going training and orientation, and communicating with caregivers about hearing aid use can also increase hearing aid use by infants and children. Case examples are used to highlight the factors that influence hearing aid use. Potential management strategies and future research needs are also discussed.

Locus of Control, Safety Attitudes and Involvement in Hazardous Events in Indian Army Aviators

2013 ◽  
Vol 3 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Catherine Joseph ◽  
Suhasini Reddy ◽  
Kanwal Kashore Sharma
Keyword(s):  
Locus Of Control ◽  
Future Research ◽  
Research Needs ◽  
Negative Attitudes ◽  
Human Errors ◽  
Safety Attitudes ◽  
Positive Correlation ◽  
Self Confidence

Locus of control (LOC), safety attitudes, and involvement in hazardous events were studied in 205 Indian Army aviators using a questionnaire-based method. A positive correlation was found between external LOC and involvement in hazardous events. Higher impulsivity and anxiety, and decreased self-confidence, safety orientation, and denial were associated with a greater number of hazardous events. Higher external LOC was associated with higher impulsivity, anxiety, and weather anxiety and with lower self-confidence, safety orientation, and denial. Internal LOC was associated with increased self-confidence, safety orientation, and denial. Hazardous events and self-confidence were higher in those involved in accidents than those not involved in accidents. Future research needs to address whether training can effectively modify LOC and negative attitudes, and whether this would cause a reduction in, and better management of, human errors.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

Exploring Antimicrobials from the Flora and Fauna of Marine: Opportunities and Limitations

2020 ◽  
Vol 17 (4) ◽  
pp. 507-514 ◽  
Author(s):  
Krishnamoorthy Venkateskumar ◽  
Subramani Parasuraman ◽  
Leow Y. Chuen ◽  
Veerasamy Ravichandran ◽  
Subramani Balamurgan
Keyword(s):  
Antimicrobial Resistance ◽  
Bioactive Compounds ◽  
Marine Environment ◽  
World Health ◽  
Future Research ◽  
Marine Biodiversity ◽  
Marine Resources ◽  
Living Space ◽  
Alternative Sources ◽  
Health Organization

About 95% of earth living space lies deep below the ocean’s surface and it harbors extraordinary diversity of marine organisms. Marine biodiversity is an exceptional reservoir of natural products, bioactive compounds, nutraceuticals and other potential compounds of commercial value. Timeline for the development of the drug from a plant, synthetic and other alternative sources is too lengthy. Exploration of the marine environment for potential bioactive compounds has gained focus and huge opportunity lies ahead for the exploration of such vast resources in the ocean. Further, the evolution of superbugs with increasing resistance to the currently available drugs is alarming and it needs coordinated efforts to resolve them. World Health Organization recommends the need and necessity to develop effective bioactive compounds to combat problems associated with antimicrobial resistance. Based on these factors, it is imperative to shift the focus towards the marine environment for potential bioactive compounds that could be utilized to tackle antimicrobial resistance. Current research trends also indicate the huge strides in research involving marine environment for drug discovery. The objective of this review article is to provide an overview of marine resources, recently reported research from marine resources, challenges, future research prospects in the marine environment.

Collaborative Remembering in Dementia: A Focus on Joint Activities

2018 ◽  
Author(s):  
Lars-Christer Hydén ◽  
Mattias Forsblad
Keyword(s):  
Everyday Life ◽  
Past Research ◽  
Research Field ◽  
Future Research ◽  
Research Needs ◽  
Joint Activities ◽  
Do So ◽  
Dementia Diseases

In this chapter we consider collaborative remembering and joint activates in everyday life in the case of people living with dementia. First, we review past research of practices that scaffolds the participation of persons with dementia in everyday chores under different stages of dementia diseases. We do so by suggesting three analytical types of scaffolding: when the scaffolding practices (i) frame the activity, (ii) guide actions, or (iii) are part of repair activities. Second, we review two aspects of collaborative remembering that are especially important in the case of dementia: training of scaffolding practices, and the sustaining and presentation of identities through collaborative storytelling. Finally, theoretical and methodological tendencies of the research field are summarized and future research needs are formulated.

scholarly journals A review of infant cry analysis and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Ji ◽  
Thosini Bamunu Mudiyanselage ◽  
Yutong Gao ◽  
Yi Pan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Signal Analysis ◽  
Future Research ◽  
Prosodic Features ◽  
Infant Cry ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Processing Techniques

AbstractThis paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

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