scholarly journals Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

2021 ◽  
Vol 7 (1) ◽  
pp. 77 ◽  
Author(s):  
Pavel Baboshkin ◽  
Natalia Yegina ◽  
Elena Zemskova ◽  
Diana Stepanova ◽  
Serhat Yuksel
Keyword(s):  
Open Innovation ◽  
Feature Reduction ◽  
Classical Approach ◽  
Agglomerative Clustering ◽  
Marginal Analysis ◽  
Machine Learning Methods ◽  
Innovation Dynamics ◽  
Patterns Of Behavior ◽  
The Mean ◽  
Innovation Indicators

This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries.

scholarly journals Eco-Innovation, Sustainability and Business Model Innovation by Open Innovation Dynamics

2021 ◽  
Vol 7 (2) ◽  
pp. 149
Author(s):  
Magdalena Pichlak ◽  
Adam R. Szromek
Keyword(s):  
Open Innovation ◽  
Business Models ◽  
Business Leaders ◽  
Innovation Activity ◽  
Computer Assisted ◽  
Supply Chain Collaboration ◽  
Sustainable Business ◽  
Innovation Activities ◽  
Innovation Dynamics ◽  
New Business

The paper aims to analyze the environmental aspects of innovation activity undertaken by companies and, in particular, to assess sustainable business leaders’ propensity to generate eco-innovation. The research described in the paper was descriptive and, to some extent, diagnostic. It was based on a non-random sample and was conducted—using the Computer Assisted Telephone Interview (CATI) method—in 2019 among 54 of the most eco-innovative Polish companies. The results of the research indicate that they are more likely to generate radical rather than incremental changes. Moreover, the most eco-innovative companies are those developing technologies for biodiversity protection. The results further indicate that companies with more than 50 employees have a higher propensity to develop incremental and radical eco-innovation than smaller firms with relatively fewer resources. Finally, this study shows that adopting an open innovation strategy strengthens the propensity to generate eco-innovation, especially radical ones. Moreover, developing such changes is dominated by the adoption of strategic and operational forward supply chain collaboration, involving the absorption of knowledge and information streaming directly from the market. The results can provide a frame for developing new business models incorporating collaboration in eco-innovation activities, especially in the situation of a post-pandemic recovery of the economy.

Effects of multiple injections of LH on the secretion of pregnane compounds from polycystic ovaries of androgen-sterilized rats

1986 ◽  
Vol 110 (3) ◽  
pp. 507-510 ◽  
Author(s):  
T. Sawada
Keyword(s):  
Pituitary Gland ◽  
Reductase Activity ◽  
Venous Blood ◽  
Classical Approach ◽  
Polycystic Ovaries ◽  
Lh Surge ◽  
Multiple Injections ◽  
Progesterone Metabolites ◽  
The Mean

ABSTRACT Differences in the secretion of pregnane compounds by follicular polycystic ovaries of androgen-sterilized rats and by normal preovulatory ovaries of early prooestrous rats were compared. Some rats were injected i.v. with LH 30 min before bleeding, in order to stimulate the secretion of steroids. This injection of LH greatly increased the secretion of progesterone, 5α-pregnane-3,20-dione and 3α-hydroxy-5α-pregnan-20-one by both types of ovaries. The response of the two progesterone metabolites in the polycystic ovaries was low, suggesting low 5α-reductase activity. Because it is known that the preovulatory LH surge is absent in androgen-sterilized rats, a classical approach was taken to circumvent the probable deficit in cyclic release of LH by giving an i.v. injection of LH (25 μg) every 4 days for 16 days. Ovarian venous blood was collected 4 days after the last injection. The mean secretion of 5α-pregnane-3,20-dione and 3α-hydroxy-5α-pregnan-20-one from the ovaries of such androgen-sterilized rats became much (P <0·01) higher than that of multiple saline-treated controls. These results suggest that low 5α-reductase activity of polycystic ovaries in androgen-sterilized rats may be due to the absence of cyclic release of LH from the pituitary gland. J. Endocr. (1986) 110, 507–510

TRAFFIC FLOWS FORECASTING BASED ON MACHINE LEARNING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Traffic Flows ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Packet Arrival ◽  
Entire Flow ◽  
The Mean ◽  
Active Flow

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

scholarly journals M&A Open Innovation, and Its Obstacle: A Case Study on GCC Region

2020 ◽  
Vol 6 (4) ◽  
pp. 138 ◽  
Author(s):  
Wardah Bindabel
Keyword(s):  
Open Innovation ◽  
Insurance Companies ◽  
Structured Interviews ◽  
Cross Culture ◽  
Sustainable Business ◽  
Gulf Region ◽  
Cross Border ◽  
Innovation Dynamics ◽  
Clear Identification ◽  
Islamic Companies

Considering the effective growth in challenges and an urge in establishment for sustainable business, companies trading globally are inclined towards the implementation of highly efficient cross-border reallocations of revolving capital. The prominent objective of this research paper is therefore the clear identification of the active key attributes and specifications of all strategic measures for efficient sustainable cross-border mergers and acquisitions (M&As) of the Islamic companies in the Gulf region that are keen to engage with the non-Islamic companies across the globe. This paper also explores the paradigm of culture, in its different manifestations, it was and still is a primary factor of creativity. This study also focuses on building some better understanding of the role of "Culture for Open Innovation Dynamics." Initially, since the need to interpret community, that can also influence the dynamics of open innovation, has sharply increased, the research addresses open innovation dynamics and its significant concerns related to cross border mergers and cross culture ventures of firms and organizations. The researcher purposively selected 15 financial institutions from the selected Gulf Cooperation Council (GCC) countries. Semi-structured interviews were conducted with 40 key individuals including Board of Directors (BOD) members, lawyers and the Shariah scholars involved with three Islamic banks and two Islamic insurance companies in GCC. The findings indicate a consensus among the respondents regarding how the Shariah corporate governance principles can present barriers for cross-border M&As. Key obstacles to the success of cross-border M&As between the Islamic and the non-Islamic companies include the Shariah compliance, weak systems of disclosure, dependency, corruption in compliance, having family members on the Board, weakened communication with external auditors, different interpretations of Shariah by different scholars and a lack of alternative Islamic financial instruments. The comprehensive research in this paper fills the research gap by specifying the key attributes of considering the future implementation and management of M&As in broader scopes.

scholarly journals Designing accurate emulators for scientific processes using calibration-driven deep models

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Bindya Venkatesh ◽  
Rushil Anirudh ◽  
Peer-Timo Bremer ◽  
Jim Gaffney ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Heterogeneous Data ◽  
Small Data ◽  
Machine Learning Methods ◽  
Squared Error ◽  
Noise Structure ◽  
The Mean ◽  
Modern Machine

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

ASYMPTOTIC BEHAVIOR OF A STOCHASTIC DELAYED CHEMOSTAT MODEL WITH NUTRIENT STORAGE

2018 ◽  
Vol 26 (02) ◽  
pp. 225-246 ◽  
Author(s):  
SHULIN SUN ◽  
XIAOFENG ZHANG
Keyword(s):  
Asymptotic Behavior ◽  
Time Delay ◽  
Function Analysis ◽  
Sufficient Conditions ◽  
Classical Approach ◽  
Nutrient Storage ◽  
Chemostat Model ◽  
Global Positive Solution ◽  
The Mean ◽  
Simulation Results

In this paper, a stochastic delayed chemostat model with nutrient storage is proposed and investigated. First, we state that there is a unique global positive solution for this stochastic system. Second, using the classical approach of Lyapunov function analysis, this stochastic delayed chemostat model is discussed in detail. We establish some sufficient conditions for the extinction of the microorganism, furthermore, we prove that the microorganism will become persistent in the mean in the chemostat under some conditions. Finally, the obtained results are illustrated by computer simulations, and simulation results reveal the effects of time delay on the persistence and extinction of the microorganism.

scholarly journals Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer’s Disease Progression

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 941
Author(s):  
Alejandro I. Trejo-Castro ◽  
Ricardo A. Caballero-Luna ◽  
José A. Garnica-López ◽  
Fernando Vega-Lara ◽  
Antonio Martinez-Torteya
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Texture Features ◽  
Mean Value ◽  
Absolute Difference ◽  
Imaging Features ◽  
Machine Learning Methods ◽  
The Mean

Early detection of Alzheimer’s disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD.

scholarly journals Exploring the locking stage of NFGAILS amyloid fibrillation via transition manifold analysis

2021 ◽  
Vol 94 (10) ◽  
Author(s):  
Andreas Bittracher ◽  
Johann Moschner ◽  
Beate Koksch ◽  
Roland Netz ◽  
Christof Schütte
Keyword(s):  
Stochastic Systems ◽  
Bond Distance ◽  
Massively Parallel ◽  
Islet Amyloid ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Hydrogen Bond Distance ◽  
Dynamical Simulations ◽  
Amyloid Fibrillation ◽  
The Mean

Abstract We demonstrate the application of the transition manifold framework to the late-stage fibrillation process of the NFGAILS peptide, a amyloidogenic fragment of the human islet amyloid polypeptide (hIAPP). This framework formulates machine learning methods for the analysis of multi-scale stochastic systems from short, massively parallel molecular dynamical simulations. We identify key intermediate states and dominant pathways of the process. Furthermore, we identify the optimally timescale-preserving reaction coordinate for the dock-lock process to a fixed pre-formed fibril and show that it exhibits strong correlation with the mean native hydrogen-bond distance. These results pave the way for a comprehensive model reduction and multi-scale analysis of amyloid fibrillation processes. Graphic Abstract

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