scholarly journals Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

2021 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Ali Arishi ◽  
Krishna K Krishnan ◽  
Vatsal Maru
Keyword(s):  
Raw Materials ◽  
Early Stage ◽  
Supply And Demand ◽  
Network Models ◽  
Combine System ◽  
Neural Network Models ◽  
Digital Twin ◽  
Twin Model ◽  
The Impact ◽  
Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Chenlu Tao ◽  
Gang Diao ◽  
Baodong Cheng
Keyword(s):  
Exchange Rate ◽  
Structural Changes ◽  
Raw Materials ◽  
Supply And Demand ◽  
Oil Prices ◽  
Control Measures ◽  
Var Model ◽  
Time Varying ◽  
Wood Industry ◽  
The Impact

China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”.

Technology, Inventing, and Patenting

2021 ◽  
Keyword(s):  
Atlantic World ◽  
Karl Marx ◽  
18Th Century ◽  
Raw Materials ◽  
Adam Smith ◽  
Supply And Demand ◽  
The United States ◽  
Early 19Th Century ◽  
Atlantic Seaboard ◽  
The Impact

The classic narrative of technology, invention, and patenting in the Atlantic world before 1850 focused on the industrialization of the Atlantic seaboard in Britain and the United States, with the adoption of mechanized cotton and wool textile production based on water power and then steam power, and on the development of related heavy industries. Other parts of the region appeared mainly as suppliers of raw materials, such as cotton from the American South, or as markets for the products of mechanized manufacture. While still a powerful narrative, most recent scholarship has reassessed or nuanced key elements, moving away from the traditional story of “heroic” inventors and toward more complex stories of supply and demand, including the capacity of economies and societies in the Atlantic world to supply the technical, commercial, and financial skills needed for invention and innovation, and the changing patterns of consumption and retail that created demand. Attention has also focused on innovation in other sectors, including armament production, transportation and public utilities, and the impact that innovation had upon the lives of those involved in it. Equally important has been a wider regional focus that now includes the southern territories of the Americas as important sites for innovation. Both Adam Smith and Karl Marx dismissed these areas of plantation agriculture as inefficient and irrelevant, a dead end compared to the centers of commerce and industry. Recent work has revised this by demonstrating the quasi-industrial processes required to process sugar, cotton, tobacco, indigo, and other tropical commodities; the scope for technological improvement; and the vast profits that enabled planters to invest in this technology. Leading plantation colonies such as Jamaica in the 18th century and Cuba in the early 19th century were among the first adopters of the steam engine outside Europe, where it had an equally transformative social and economic impact.

Research on Optimization of Multi-Commodity Supply and Demand Logistics Network Based on Supernetwork

2018 ◽  
Vol 16 (3) ◽  
pp. 78-93
Author(s):  
Yongtao Peng ◽  
Yaya Li ◽  
Meiling He
Keyword(s):  
Supply And Demand ◽  
Quantitative Description ◽  
Network Models ◽  
Projection Algorithm ◽  
Logistics Network ◽  
Matching Degree ◽  
Supply Capacity ◽  
Logistics Demand ◽  
The Impact ◽  
The Relationship

For the realization, a qualitative and quantitative description of matching degree between the elements for logistics supply network and demand network, logistics super network models are constructed by the theory of super network. Faced with the problems of diverse demand and massive circulation for commodities, this article studies the structure of the logistics super network of multi-commodity circulation and establishes the continuous cost function of the logistics demand and supply, reflecting the logistics cost of different commodities in different phrases. This article aims to establish the optimization model of logistics supernetwork by aiming to maximize the matching of supply and demand of multi-commodity. The model is transformed into the variational inequality problem, and proves the existence and uniqueness of the equivalence solution. Use the case of the logistics supernetwork of coal, a modified projection algorithm is adopted and the fact is revealed that improving the supply capacity of the network matching may have the original 81.3% increase to 90.5%, improving the impact of the relationship between trades, matching degree can be increased to 90.1%.

Mathematics anxiety and cognition: an integrated neural network model

2020 ◽  
Vol 31 (3) ◽  
pp. 287-296
Author(s):  
Ahmed A. Moustafa ◽  
Angela Porter ◽  
Ahmed M. Megreya
Keyword(s):  
Neural Network ◽  
Educational Outcomes ◽  
Mathematics Anxiety ◽  
Network Models ◽  
Neural Mechanism ◽  
Neural Network Models ◽  
Cognitive Studies ◽  
Behavioral Studies ◽  
Negative Effect ◽  
The Impact

AbstractMany students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.

Identification of Tomato Leaf Disease Detection using Pretrained Deep Convolutional Neural Network Models

2020 ◽  
Vol 21 (4) ◽  
pp. 625-635
Author(s):  
Anandhakrishnan T ◽  
Jaisakthi S.M Murugaiyan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Computing Time ◽  
Network Models ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Leaf Disease ◽  
Proposed Model ◽  
Deep Cnn

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability. The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decreasein quality. And Xception also generated a fine 99.45% precision in less computing time.

scholarly journals The Impact of Informatization of Society on the Labor Market

2021 ◽  
Vol 12 (3Sup1) ◽  
pp. 155-167
Author(s):  
Oleksandr Yashchyk ◽  
◽  
Valentyna Shevchenko ◽  
Viktoriia Kiptenko ◽  
Oleksandra Razumova ◽  
...  
Keyword(s):  
Labor Market ◽  
Government Regulation ◽  
Structural Changes ◽  
Raw Materials ◽  
Supply And Demand ◽  
Working Hours ◽  
Industrial Society ◽  
Information And Computer Technology ◽  
Cloud Technologies ◽  
The Impact

This article examines the transformation of the labor market under the influence of informatization of society. It is noted that in the conditions of globalization and informatization of the nowadays a post-industrial society has been formed, in which information is a determining factor of production. New opportunities and challenges of the labor market in the conditions of information society development are analyzed. The informatization of society changes the conditions, nature and forms of work. Extensive digitalization, the use of cloud technologies and artificial intelligence systems are displacing traditional forms of employment towards teleworking, which makes workers more mobile and able to optimize working hours. It is established that the spread of technology increases the efficiency of the recruitment and searching job processes. Informatization of society contributes to the creation of a digital labor market, which forms the demand and supply of information and computer technology workers. In the context of informatization of society, the labor market is characterized by an imbalance between supply and demand of labor due to structural changes in the economy. Among the challenges of the labor market are rising unemployment in the raw materials industries, robotics and automation of routine manual labor. The digitalization of the economy leads to the need to adjust government regulation of business and provide social guarantees for employees. It is noted that the informatization of society provides more benefits to the labor market than obstacles. Solving the problems it raises, promotes progress and economic development.

scholarly journals The Impact of an Urban Growth Boundary on Land Development in Knox County, Tennessee: A Comparison of Two-Stage Probit Least Squares and Multilayer Neural Network Models

2007 ◽  
Vol 39 (3) ◽  
pp. 701-717 ◽  
Author(s):  
Seong-Hoon Cho ◽  
Olufemi A. Omitaomu ◽  
Neelam C. Poudyal ◽  
David B. Eastwood
Keyword(s):  
Neural Network ◽  
Urban Growth ◽  
Land Development ◽  
Network Models ◽  
Probit Model ◽  
Neural Network Models ◽  
Two Stage ◽  
Urban Growth Boundary ◽  
Knox County ◽  
The Impact

The impact of an urban growth boundary (UGB) on land development in Knox County, TN is estimated via two-stage probit and neural-network models. The insignificance of UGB variable in the two-stage probit model and more visible development patterns in the western part of Knoxville and the neighboring town of Farragut during the post-UGB period in both models suggest that the UGB has not curtailed urban sprawl. Although the network model is found to be a viable alternative to more conventional discrete choice approach for improving the predictability of land development, it is at the cost of evaluating marginal effects.

A Gaussian process–based approach to cope with uncertainty in structural health monitoring

2016 ◽  
Vol 16 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Hessamodin Teimouri ◽  
Abbas S. Milani ◽  
Jason Loeppky ◽  
Rudolf Seethaler
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Gaussian Process ◽  
Health Monitoring ◽  
Network Models ◽  
Neural Network Models ◽  
Structural Health ◽  
Covariance Functions ◽  
Industrial Sectors ◽  
The Impact

Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing “robust” structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined.

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