scholarly journals Perception of Daily Time: Insights from the Fruit Flies

Insects ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Joydeep De ◽  
Abhishek Chatterjee
Keyword(s):  
Neural Network ◽  
Biological Diversity ◽  
Internal Clock ◽  
Time Of Day ◽  
Mental Maps ◽  
The Core ◽  
Drosophila Brain ◽  
Annual Time ◽  
Daily Time ◽  
Compute Time

We create mental maps of the space that surrounds us; our brains also compute time—in particular, the time of day. Visual, thermal, social, and other cues tune the clock-like timekeeper. Consequently, the internal clock synchronizes with the external day-night cycles. In fact, daylength itself varies, causing the change of seasons and forcing our brain clock to accommodate layers of plasticity. However, the core of the clock, i.e., its molecular underpinnings, are highly resistant to perturbations, while the way animals adapt to the daily and annual time shows tremendous biological diversity. How can this be achieved? In this review, we will focus on 75 pairs of clock neurons in the Drosophila brain to understand how a small neural network perceives and responds to the time of the day, and the time of the year.

IDENTIFICATION OF COHERENT STRUCTURES IN TURBULENT SHEAR FLOWS WITH A FUZZY ARTMAP NEURAL NETWORK

1996 ◽  
Vol 07 (05) ◽  
pp. 559-568 ◽  
Author(s):  
J. FERRE-GINE ◽  
R. RALLO ◽  
A. ARENAS ◽  
FRANCE GIRALT
Keyword(s):  
Neural Network ◽  
Template Matching ◽  
Phase Relationship ◽  
Fuzzy Artmap ◽  
Turbulent Shear ◽  
Training Procedure ◽  
Full Data ◽  
The Core ◽  
Data Files ◽  
Number Of Classes

An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.

scholarly journals A Genomic Distance Based on MUM Indicates Discontinuity between Most Bacterial Species and Genera

2008 ◽  
Vol 191 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Marc Deloger ◽  
Meriem El Karoui ◽  
Marie-Agnès Petit
Keyword(s):  
Dna Sequences ◽  
Dna Content ◽  
Core Genome ◽  
Biological Diversity ◽  
Bacterial Species ◽  
Genomic Distance ◽  
The Core ◽  
Intraspecies Diversity ◽  
Genome Level ◽  
Definition Of

ABSTRACT The fundamental unit of biological diversity is the species. However, a remarkable extent of intraspecies diversity in bacteria was discovered by genome sequencing, and it reveals the need to develop clear criteria to group strains within a species. Two main types of analyses used to quantify intraspecies variation at the genome level are the average nucleotide identity (ANI), which detects the DNA conservation of the core genome, and the DNA content, which calculates the proportion of DNA shared by two genomes. Both estimates are based on BLAST alignments for the definition of DNA sequences common to the genome pair. Interestingly, however, results using these methods on intraspecies pairs are not well correlated. This prompted us to develop a genomic-distance index taking into account both criteria of diversity, which are based on DNA maximal unique matches (MUM) shared by two genomes. The values, called MUMi, for MUM index, correlate better with the ANI than with the DNA content. Moreover, the MUMi groups strains in a way that is congruent with routinely used multilocus sequence-typing trees, as well as with ANI-based trees. We used the MUMi to determine the relatedness of all available genome pairs at the species and genus levels. Our analysis reveals a certain consistency in the current notion of bacterial species, in that the bulk of intraspecies and intragenus values are clearly separable. It also confirms that some species are much more diverse than most. As the MUMi is fast to calculate, it offers the possibility of measuring genome distances on the whole database of available genomes.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

scholarly journals The Research of Temperature Compensation for Thermopile Sensor Based on Improved PSO-BP Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuanjiang Li ◽  
Yuehua Li ◽  
Feng Li ◽  
Bin Zhao ◽  
QingQing Li
Keyword(s):  
Neural Network ◽  
Thermal Radiation ◽  
Bp Neural Network ◽  
Temperature Compensation ◽  
Safety Monitoring ◽  
Bp Algorithm ◽  
Bp Network ◽  
The Core ◽  
Thermopile Sensor ◽  
Improved Pso

When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.

An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
David Palchak ◽  
Siddharth Suryanarayanan ◽  
Daniel Zimmerle
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Time Of Day ◽  
University Campus ◽  
Electrical Load ◽  
Ann Model ◽  
Short Term ◽  
Management Options ◽  
Artificial Neural

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error; the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-h period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of noncritical loads, and availability of time of use (ToU) pricing, the possible demand-side management options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

Part V Subject Matter, Ch.31 The Protection of the Marine Environment Pollution and Fisheries

2021 ◽  
Author(s):  
Fabra Adriana
Keyword(s):  
Marine Environment ◽  
Biological Diversity ◽  
Marine Pollution ◽  
Case Law ◽  
Habitat Destruction ◽  
Marine Biodiversity ◽  
Technological Changes ◽  
The Core ◽  
Sources Of Pollution

This chapter begins by looking at the role of the 1982 UN Law of the Sea Convention (UNCLOS) as the framework legal instrument on the oceans. Indeed, the UNCLOS is one of the most significant international law instruments of all time and is at the core of today's governance of the oceans. UNCLOS is a product of the time when it was negotiated, which brought together a desire to provide global stability to competing jurisdictional claims over the oceans and devise solutions to rapidly increasing rates of marine pollution. However, technological changes and increased or unforeseen sources of pollution and habitat destruction have exposed some of the Convention's limitations, which derive from a fragmented perspective of the marine environment, and a failure to address the interaction between different ocean uses and marine stressors and provide rules on the conservation of marine biological diversity. The chapter then evaluates global and regional treaty requirements, soft law instruments, and case law concerning the protection of the marine environment from various sources of pollution, and the conservation of marine living resources, with a focus on fisheries, and the protection of marine biodiversity.

Experimenting with Recognition Accelerator for Pavement Distress Identification

1996 ◽  
Vol 1536 (1) ◽  
pp. 130-135 ◽  
Author(s):  
Sudhakar Nallamothu ◽  
Kelvin C. P. Wang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coulomb Energy ◽  
Processing Unit ◽  
Basic Principles ◽  
Pavement Distress ◽  
The Core ◽  
Artificial Neural Network Ann ◽  
Computer Board

A study was conducted using a computer board embedded with an artificial neural network (ANN) microchip for pattern recognition of pavement distress classification. The basic principles behind ANNs and pattern recognition are discussed. The hardware architecture of the Ni1000 recognition accelerator chip, which is the core of the ANN computer board, is presented, and the principle of operation of the restricted coulomb energy algorithm used in the chip is discussed. It is demonstrated that the Ni1000 Recognition Accelerator chip can be used for pattern recognition of pavement distress. Distresses in pavement images have been successfully classified using the Ni1000 recognition accelerator. The Ni1000 has the potential to be used as the core processing unit for distress classification at highway speeds.

scholarly journals Daily activity recognition based on recurrent neural network using multi-modal signals

2018 ◽  
Vol 7 ◽  
Author(s):  
Akira Tamamori ◽  
Tomoki Hayashi ◽  
Tomoki Toda ◽  
Kazuya Takeda
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Recurrent Neural Network ◽  
Bottom Layer ◽  
Prototype System ◽  
Recording Device ◽  
Feed Forward Neural Network ◽  
The Core ◽  
Linear Transform ◽  
Indoor And Outdoor

Our aim is to develop a smartphone-based life-logging system. Human activity recognition (HAR) is one of the core techniques to realize it. Recent studies reported the effectiveness of feed-forward neural network (FF-NN) and recurrent neural network (RNN) as a classifier for HAR task. However, there are still unresolved problems in those studies: (1) a life-logging system using only a smartphone for recording device has not been developed, (2) only indoor activities have been utilized for evaluation, (3) insufficient investigations/evaluations of RNN. In this study, we address these unresolved problems as follows: (1) we build a prototype system for life-logging and conduct data recording experiment on this system to include both indoor and outdoor activities. The experimental results of HAR on this new dataset showed that RNN-based classifier was still effective. (2) From the results of a HAR experiment, it was demonstrated that a multi-layered Simple Recurrent Unit with a non-linear transform at the bottom layer and a highway-connection was the most effective. (3) We could grasp the reason for the improvement of RNN from FF-NN by observing the posterior probabilities over test data.

Neural Network Vision-Guided Mobile Robot for Retrieving Driving-Range Golf Balls

2006 ◽  
Vol 10 (2) ◽  
pp. 181-186 ◽  
Author(s):  
Elmer P. Dadios ◽  
◽  
Kaoru Hirota ◽  
Michelle L. Catigum ◽  
Albert C. Gutierrez ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Open Field ◽  
Vision System ◽  
Time Of Day ◽  
Autonomous Mobile Robot ◽  
Rotating Blades ◽  
Driving Range

We developed an autonomous mobile robot with neural network (NN) vision that searches for and collects golf balls on an open or an indoor golf driving range. The robot recognizes range borderlines by red stripes. Scattered golf balls are collected using mechanically designed rotating blades. The NN vision identifies objects that are not golf balls and prevents the robot from picking them. The vision system is robust enough to navigate an open field and pick up the golf balls any time of day. Results of the experiments showed that our proposal operates accurately and reliably.

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