scholarly journals Current achievements and future developments of a novel AI based visual monitoring of beehives in ecotoxicology and for the monitoring of landscape structures

2020 ◽  
Author(s):  
Frederic Tausch ◽  
Katharina Schmidt ◽  
Matthias Diehl
Keyword(s):  
Honey Bees ◽  
Large Scale ◽  
Image Data ◽  
Visual Monitoring ◽  
Future Developments ◽  
Related Information ◽  
The Central Nervous System ◽  
Visual Monitoring System ◽  
Study Designs ◽  
Shed Light

Abstract in EnglishHoney bees are valuable bio-indicators. As such, they hold a vast potential to help shed light on the extent and interdependencies of factors influencing the decline in the number of insects. However, to date this potential has not yet been fully leveraged, as the production of reliable data requires large-scale study designs, which are very labour intensive and therefore costly.A novel Artificial Intelligence (AI) based visual monitoring system could enable the partial automatization of data collection on activity, forager loss and impairment of the central nervous system. The possibility to extract features from image data could prospectively also allow an assessment of pollen intake and a differentiation of dead bees, drones and worker bees as well as other insects such as wasps or hornets.The technology was validated in different studies with regards to its scalability and its ability to extract motion and feature related information.The prospective possibilities were analyzed regarding their potential to enable advances both within ecotoxicological research and the monitoring of pollinator habitats.

scholarly journals Neuroscience in the blink of an eye: using the retina to understand the brain

2020 ◽  
Vol 42 (5) ◽  
pp. 18-24
Author(s):  
Nicholas E. Albrecht ◽  
Courtney A. Burger ◽  
Melanie A. Samuel
Keyword(s):  
Complex System ◽  
Central Nervous System ◽  
Nervous System ◽  
Large Scale ◽  
The Central Nervous System ◽  
The Brain ◽  
Computational Properties ◽  
Shed Light

Over the centuries, artists, poets, writers and scientists have all attempted to answer a key existential question: what makes us human? Neuroscience has provided us with one exciting possible answer: our brains. To decode the complexities of the brain, many large-scale efforts are aimed at unravelling the cellular, molecular and computational properties of this startlingly complex system. Yet, to date, many important insights towards these problems have come from a surprisingly humble part of the central nervous system – the retina. The retina resides outside the skull within the eye and is responsible for vision. It contains diverse neuron types that detect light and has proven to be a uniquely approachable system for discovering neurobiology principles owing to its inherent organization, wiring and experimental accessibility. In this article, we describe how the retina has been used to make key neuroscience discoveries, and in turn how these principles shed light on how the brain works.

scholarly journals Neuroticism and Emotional Risk During the COVID-19 Pandemic

2020 ◽  
Author(s):  
Lara Kroencke ◽  
Katharina Geukes ◽  
Till Utesch ◽  
Niclas Kuper ◽  
Mitja Back
Keyword(s):  
Negative Affect ◽  
Personality Trait ◽  
Large Scale ◽  
Affective Responses ◽  
Daily Lives ◽  
Affective Reactivity ◽  
Related Information ◽  
Psychological Maladjustment ◽  
Shed Light

Large-scale health crises, such as the COVID-19 pandemic, may evoke negative affective responses, which are linked to psychological maladjustment and psychopathology. Here, we shed light on the role of the personality trait neuroticism in predicting who experiences negative affective responses. In a large-scale experience-sampling study (N = 1,609; 38,120 momentary reports), we showed that individuals high in neuroticism experienced more negative affect and higher affective variability in their daily lives. Individuals high in neuroticism also (a) paid more attention to COVID-19-related information and worried more about the consequences of the pandemic (crisis preoccupation), and (b) experienced more negative affect during this preoccupation (affective reactivity). These findings offer new insights into the consequences and dynamics of neuroticism in extreme environmental contexts.

Development of a Visual Monitoring System for Deformation Measuring of Welded Members and its Application

2008 ◽  
Vol 580-582 ◽  
pp. 557-560 ◽  
Author(s):  
J.G. Han ◽  
Kyong Ho Chang ◽  
Gab Chul Jang ◽  
K.K. Hong ◽  
Sam Deok Cho ◽  
...  
Keyword(s):  
Monitoring System ◽  
Three Dimensional ◽  
Measuring Method ◽  
Welding Residual Stress ◽  
Measuring System ◽  
Visual Monitoring ◽  
Test Error ◽  
Steel Members ◽  
Loading Tests ◽  
Visual Monitoring System

Recently, in the loading tests for steel members, the deformation value is measured by calculating a distance of both cross-heads. This measuring method encounters a test error due to various environmental factors, such as initial slip, etc.. Especially, in the case of welded members, the non-uniform deformation behavior in welded joints is observed because of the effect of welding residual stress and weld metal. This is mainly responsible for a test error and a loss of the reliability for used test instruments. Therefore, to improve the accuracy and the applicability of measuring system, it is necessary to employ a visual monitoring system which can accurately measure the local and overall deformation of welded members. In this paper, to accurately measure a deformation of welded members, a visual monitoring system (VMS) was developed by using three-dimensional digital photogrammetry. The VMS was applied to the loading tests of a welded member. The accuracy and the applicability of VMS was verified by comparing to the deformation value measured by a test instrument (MTS-810). The characteristics of the behavior near a welded joint were investigated by using VMS.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

scholarly journals Obsessive–compulsive symptoms and information seeking during the Covid-19 pandemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alisa M. Loosen ◽  
Vasilisa Skvortsova ◽  
Tobias U. Hauser
Keyword(s):  
Mental Health ◽  
Information Seeking ◽  
Large Scale ◽  
Psychiatric Symptoms ◽  
Longitudinal Change ◽  
Depression Symptoms ◽  
Obsessive Compulsive ◽  
Related Information ◽  
Pandemic Wave

AbstractIncreased mental-health symptoms as a reaction to stressful life events, such as the Covid-19 pandemic, are common. Critically, successful adaptation helps to reduce such symptoms to baseline, preventing long-term psychiatric disorders. It is thus important to understand whether and which psychiatric symptoms show transient elevations, and which persist long-term and become chronically heightened. At particular risk for the latter trajectory are symptom dimensions directly affected by the pandemic, such as obsessive–compulsive (OC) symptoms. In this longitudinal large-scale study (N = 406), we assessed how OC, anxiety and depression symptoms changed throughout the first pandemic wave in a sample of the general UK public. We further examined how these symptoms affected pandemic-related information seeking and adherence to governmental guidelines. We show that scores in all psychiatric domains were initially elevated, but showed distinct longitudinal change patterns. Depression scores decreased, and anxiety plateaued during the first pandemic wave, while OC symptoms further increased, even after the ease of Covid-19 restrictions. These OC symptoms were directly linked to Covid-related information seeking, which gave rise to higher adherence to government guidelines. This increase of OC symptoms in this non-clinical sample shows that the domain is disproportionately affected by the pandemic. We discuss the long-term impact of the Covid-19 pandemic on public mental health, which calls for continued close observation of symptom development.

scholarly journals System design for inferring colony-level pollination activity through miniature bee-mounted sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haron M. Abdel-Raziq ◽  
Daniel M. Palmer ◽  
Phoebe A. Koenig ◽  
Alyosha C. Molnar ◽  
Kirstin H. Petersen
Keyword(s):  
Data Acquisition ◽  
Honey Bees ◽  
Large Scale ◽  
Physical World ◽  
Cluttered Environments ◽  
Robotic Mapping ◽  
Large Scale Data ◽  
Trade Offs ◽  
Large Numbers ◽  
Engineered Systems

AbstractIn digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.

Brain stem – from general view to computational model based on switchboard rules of operation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Włodzisław Duch ◽  
Dariusz Mikołajewski
Keyword(s):  
Brain Stem ◽  
Large Scale ◽  
Ionic Channels ◽  
General View ◽  
The Central Nervous System ◽  
Network States ◽  
Large Scale Simulations ◽  
Influence Of Noise ◽  
The Brain ◽  
Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

Experimental modeling and clinical technology of photodynamic therapy

2020 ◽  
pp. 72-80
Author(s):  
Daria Aleksandrovna Krapivnitskaya ◽  
Kseniya Vyacheslavovna Kuznetsova ◽  
Igor Valentinovich Barskov ◽  
Vladimir Germanovich Taktarov ◽  
Vladimir Yurievich Pereverzev
Keyword(s):  
Central Nervous System ◽  
Photodynamic Therapy ◽  
Large Scale ◽  
Clinical Medicine ◽  
Practical Importance ◽  
Skin Lesions ◽  
Clinical Aspects ◽  
Regenerative Processes ◽  
Ischemic Tissue ◽  
The Central Nervous System

In recent years, the amount of large-scale experimental and clinical studies has increased considerably leading to the development of techniques and their widespread use both in their field and serving as a basis for the combination of even paradoxically incompatible areas of experimental and clinical medicine. The authors consider one of the main objectives of this work to identify a stable correlation between experimental pathomorphological study in ischemic tissue lesion and a therapeutic effect in dermatology in various pathological processes since the fundamental method in both cases is represented by a photochemical effect on the central nervous system and skin. These studies are not only of theoretical value but also of great practical importance both for neurological (search for medicines used to stimulate regenerative processes in ischemic pathology) and dermatological clinical aspects (ablation method of photodynamic therapy for various skin lesions).

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