The behavior propagation and confrontation competition model based on information energy

2021 ◽  
pp. 2150281
Author(s):  
Zhongkai Dang ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jiaxuan Zhang
Keyword(s):  
Large Scale ◽  
Information Dissemination ◽  
Early Stage ◽  
Group Behavior ◽  
Competition Model ◽  
Edge Weight ◽  
Model Based ◽  
Related Information ◽  
Information Energy ◽  
Behavioral Information

In recent years, the influence of group behavior on individual behavior has been widely concerned by people. This paper takes the propagation of behavior-related information as the influencing medium, constructs the behavior propagation and confrontation competition model based on information energy, and studies the propagation of single behavior and confrontation behaviors in the network. In the model, the behavioral information energy on nodes is related to the information edge weight and the length of the reachable path between nodes, and we quantify the propagation probability of behaviors and the competition probability between the confrontation behaviors by the behavioral information energy. Through modeling and analyzing the change of node motivation and the behavior propagation rule, we carry out the simulation in small-world network and BA scale-free network, respectively. Experimental results show that the information energy has a promoting effect on the propagation of behaviors, and the behaviors that erupt in the network generally have an important information dissemination basis in the early stage and may be promoted by relevant information in the later stage. An open and tightly connected network environment is easier to promote the large-scale propagation of a new behavior. In addition, when two kinds of confrontation behaviors occur in the crowd network, the propagation of behavior-related information and the maintenance of people’s interest in the behavior are the key to the success of the behavioral competition.

Model-based Control Techniques for Large-Scale High-Precision Stage

2020 ◽  
Vol 140 (4) ◽  
pp. 272-280
Author(s):  
Wataru Ohnishi ◽  
Hiroshi Fujimoto ◽  
Koichi Sakata
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Model Based Control ◽  
Precision Stage ◽  
Control Techniques ◽  
Model Based

Konkurrenz zwischen Stieleiche und Buche auf Lothar-Sturmflächen | Competition between pedunculate oak and European beech on Lothar windthrow areas

2009 ◽  
Vol 160 (5) ◽  
pp. 114-123 ◽  
Author(s):  
Daniel Otto ◽  
Sven Wagner ◽  
Peter Brang
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Tree Height ◽  
European Beech ◽  
Threshold Value ◽  
Test Methods ◽  
Permanent Plots ◽  
Pedunculate Oak ◽  
Competitive Pressure ◽  
Competition Index

The competitive pressure of naturally regenerated European beech (Fagus sylvatica) saplings on planted pedunculate oak (Quercus robur) was investigated on two 1.8 ha permanent plots near Habsburg and Murten (Switzerland). The plots were established with the aim to test methods of artificial oak regeneration after large-scale windthrow. On both plots, 80 oaks exposed to varying levels of competitive pressure from at most 10 neighbouring beech trees were selected. The height of each oak as well as stem and branch diameters were measured. The competitive pressure was assessed using Schütz's competition index, which is based on relative tree height, crown overlap and distance from competing neighbours. Oak trees growing without or with only slight competition from beech were equally tall, while oaks exposed to moderate to strong competition were smaller. A threshold value for the competition index was found above which oak height decreased strongly. The stem and branch diameters of the oaks started to decrease even if the competition from beech was slight, and decreased much further with more competition. The oak stems started to become more slender even with only slight competition from beech. On the moderately acid beech sites studied here, beech grow taller faster than oak. Thus where beech is competing with oak and the aim is to maintain the oak, competitive pressure on the oak must be reduced at an early stage. The degree of the intervention should, however, take the individual competitive interaction into account, with more intervention if the competition is strong.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

scholarly journals Favorable Effects of GLP-1 Receptor Agonist against Pancreatic β-Cell Glucose Toxicity and the Development of Arteriosclerosis: “The Earlier, the Better” in Therapy with Incretin-Based Medicine

2021 ◽  
Vol 22 (15) ◽  
pp. 7917
Author(s):  
Hideaki Kaneto ◽  
Tomohiko Kimura ◽  
Masashi Shimoda ◽  
Atsushi Obata ◽  
Junpei Sanada ◽  
...  
Keyword(s):  
Large Scale ◽  
Cell Function ◽  
Apoptotic Cell ◽  
Early Stage ◽  
Insulin Biosynthesis ◽  
Protective Effects ◽  
Pancreatic Β Cell ◽  
Β Cells ◽  
Β Cell ◽  
Glucose Toxicity

Fundamental pancreatic β-cell function is to produce and secrete insulin in response to blood glucose levels. However, when β-cells are chronically exposed to hyperglycemia in type 2 diabetes mellitus (T2DM), insulin biosynthesis and secretion are decreased together with reduced expression of insulin transcription factors. Glucagon-like peptide-1 (GLP-1) plays a crucial role in pancreatic β-cells; GLP-1 binds to the GLP-1 receptor (GLP-1R) in the β-cell membrane and thereby enhances insulin secretion, suppresses apoptotic cell death and increase proliferation of β-cells. However, GLP-1R expression in β-cells is reduced under diabetic conditions and thus the GLP-1R activator (GLP-1RA) shows more favorable effects on β-cells at an early stage of T2DM compared to an advanced stage. On the other hand, it has been drawing much attention to the idea that GLP-1 signaling is important in arterial cells; GLP-1 increases nitric oxide, which leads to facilitation of vascular relaxation and suppression of arteriosclerosis. However, GLP-1R expression in arterial cells is also reduced under diabetic conditions and thus GLP-1RA shows more protective effects on arteriosclerosis at an early stage of T2DM. Furthermore, it has been reported recently that administration of GLP-1RA leads to the reduction of cardiovascular events in various large-scale clinical trials. Therefore, we think that it would be better to start GLP-1RA at an early stage of T2DM for the prevention of arteriosclerosis and protection of β-cells against glucose toxicity in routine medical care.

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