scholarly journals Relationship between Urban New Business Indexes and the Business Environment of Chinese Cities: A Study Based on Entropy-TOPSIS and a Gaussian Process Regression Model

2020 ◽  
Vol 12 (24) ◽  
pp. 10422
Author(s):  
Yishao Shi ◽  
Danxuan Liu
Keyword(s):  
Gaussian Process ◽  
Explanatory Power ◽  
Gaussian Process Regression ◽  
Business Environment ◽  
Electronic Payment ◽  
Development Level ◽  
New Business ◽  
Order Preference ◽  
Commercial Landscape ◽  
The Relationship

The interactive development of economic globalization, informatization, marketization, and urbanization has reshaped the urban commercial landscape and society, and poses new requirements for the business environment. New commerce forms that are based on information technology and electronic payment and integrate online and offline forms are growing rapidly in China. However, the relationship between new commerce forms and the business environment has not received sufficient academic attention. Using 29 major cities in China, this paper constructs a new business index system consisting of the following six sub-indexes: the characteristic hotels index, the Starbucks index, the Freshhema index, the concept bookstores index, the smart convenience stores index, and the healthcare and medical examination index. The entropy coupled with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was used for quantitative evaluation of urban new business vitality. We found that the Freshhema index and smart convenient store index are the two most important evaluation factors. The relationship between the new business index and the business environment was examined through multiple linear regression (MLR) and Gaussian process regression (GPR) analysis. We found that the MLR is not a valid model, and instead, the nonlinear GPR model has good explanatory power for this relationship. The results show that human capital has a more important effect than the economic development level on business vitality. The rise and development of new commercial forms depend on the innovation and optimization of the business environment.

scholarly journals The Variational Gaussian Approximation Revisited

2009 ◽  
Vol 21 (3) ◽  
pp. 786-792 ◽  
Author(s):  
Manfred Opper ◽  
Cédric Archambeau
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Learning Community ◽  
Gaussian Approximation ◽  
Good Reason ◽  
Random Variables ◽  
Gaussian Process Regression ◽  
Variational Approximation ◽  
Posterior Distributions ◽  
The Relationship

The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to the corresponding approximation by factorizing distributions. This is for a good reason: the gaussian approximation is in general plagued by an [Formula: see text] number of variational parameters to be optimized, N being the number of random variables. In this letter, we discuss the relationship between the Laplace and the variational approximation, and we show that for models with gaussian priors and factorizing likelihoods, the number of variational parameters is actually [Formula: see text]. The approach is applied to gaussian process regression with nongaussian likelihoods.

scholarly journals A framework for understanding the role of culture in entrepreneurship

2007 ◽  
Vol 7 (1) ◽  
Author(s):  
B. Urban
Keyword(s):  
Cultural Values ◽  
Contextual Factors ◽  
Business Environment ◽  
Entrepreneurial Activity ◽  
Future Research ◽  
New Business ◽  
Antecedents And Consequences ◽  
Entrepreneurial Action ◽  
Business Regulations ◽  
The Relationship

Purpose: Synthesising research findings on business regulations, culture, self, and entrepreneurship, this article provides a broad overview of the potential patterns of relationships between cultural values, personal and contextual factors, and entrepreneurial outcomes. Theories of entrepreneurship where either environmental or personality variables have been specified as unique predictors of entrepreneurship are investigated to determine whether they capture the complexity of entrepreneurial action that encompasses the interaction of environmental, cognitive, and behavioural variables. Emphasis is also placed on the South African business environment, where business regulations that may enhance or constrain new business activity are analysed. Design/Methodology/Approach: Building on previous conceptualisations and empirical findings, the article identifies salient antecedents and consequences of venture creation from established literature. A framework is then proposed, building on previous findings to approach the interaction between the multiple interacting influences on entrepreneurship more systematically.Findings: Principal literature reviews indicate that, despite SA's apparent favourable regulatory environment, low entrepreneurial activity persists, and understanding the interplay between culture, self, context and entrepreneurship remains imperative for policymakers and practitioners. In the proposed model, cultural values affect the perception of an individual resulting in key entrepreneurial outcomes; culture is depicted as a moderator in the relationship between contextual factors (business regulations) and entrepreneurial outcomes, and acts as a catalyst rather than a causal agent of entrepreneurial outcomes. Limitations include lack of any causal inferences, and thus directionality between the variables which are not fully explored or empirical tested.Implications: Implications for policymakers encouraging entrepreneurship in SA, are that the complexity of factors involved in enhancing or constraining entrepreneurship should be given due consideration, without any one set of variables overshadowing the other factors. Entrepreneurs, educators, and consultants all benefit from a better understanding of how various factors merge into the intent to start a business. Training entrepreneurs to be aware of the multiple influencing factors will raise their level of sophistication and ability to correctly gauge opportunities. Originality/Value: Since no unified theme exists regarding the relationship between culture / self / context and entrepreneurship, the synthesis of the variables proposed in this framework offers an introductory roadmap to guide future research. Taking the multiplicity of variables and dimensions influencing entrepreneurial activity even further, the article provides crucial insights of how entrepreneurial outcomes are determined in a SA context; such models are essential for real advances in the emerging field of entrepreneurship.

scholarly journals Leaf Anthocyanin Content Retrieval with Partial Least Squares and Gaussian Process Regression from Spectral Reflectance Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3078
Author(s):  
Yingying Li ◽  
Jingfeng Huang
Keyword(s):  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Gaussian Process Regression ◽  
Research Field ◽  
Anthocyanin Content ◽  
Least Squares Regression ◽  
Content Retrieval ◽  
First Time ◽  
The Relationship

Leaf pigment content retrieval is an essential research field in remote sensing. However, retrieval studies on anthocyanins are quite rare compared to those on chlorophylls and carotenoids. Given the critical physiological significance of anthocyanins, this situation should be improved. In this study, using the reflectance, partial least squares regression (PLSR) and Gaussian process regression (GPR) were sought to retrieve the leaf anthocyanin content. To our knowledge, this is the first time that PLSR and GPR have been employed in such studies. The results showed that, based on the logarithmic transformation of the reflectance (log(1/R)) with 564 and 705 nm, the GPR model performed the best (R2/RMSE (nmol/cm2): 0.93/2.18 in the calibration, and 0.93/2.20 in the validation) of all the investigated methods. The PLSR model involved four wavelengths and achieved relatively low accuracy (R2/RMSE (nmol/cm2): 0.87/2.88 in calibration, and 0.88/2.89 in validation). GPR apparently outperformed PLSR. The reason was likely that the non-linear property made GPR more effective than the linear PLSR in characterizing the relationship for the absorbance vs. content of anthocyanins. For GPR, selected wavelengths around the green peak and red edge region (one from each) were promising to build simple and accurate two-wavelength models with R2 > 0.90.

Technology transfer from British to Vietnamese industrial companies - venturing into a new business culture

2018 ◽  
Vol 34 (2) ◽  
Author(s):  
Ca Tran Ngoc
Keyword(s):  
Technology Transfer ◽  
Transfer Process ◽  
Oil And Gas ◽  
Service Industry ◽  
Oil And Gas Industry ◽  
Business Environment ◽  
Background Information ◽  
Business Culture ◽  
Industrial Companies ◽  
New Business

The paper examines the process of technology transfer from British industrial companies to Vietnamese companies, to look at the obstacles of this process, especially in dealing with different business culture environments. The study uses the case studies method, conducting interviews with about ten companies working in oil and gas service industry. Since this is only a first stage of the longer term project, only preliminary results were discussed. Therefore, a company in civil engineering consulting has been examined for comparison. The paper argues that the differences in perception of the same operation activity like service in oil and gas industry are crucial factors to take into account if the transfer process is to be successful. Also, the transferor and the recipient may have different behaviour in negotiating, in communicating with each other. Thus, the preparation of background information, to do "home work", patience and pro-active attitudes in trying to understand partners are important for transferring technology into different business environment.   In addition, the factors, sometime not very technology-related, such as internal political motives and organisational issues of the firms involved can be very influential in the success of technology transfer process.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

2018 ◽  
Author(s):  
Caitlin C. Bannan ◽  
David Mobley ◽  
A. Geoff Skillman
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Molecular Graph ◽  
Gaussian Process Regression ◽  
Ionization State ◽  
Training Set ◽  
Physiochemical Properties ◽  
Quantile Plots ◽  
Physical And Chemical ◽  
Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

Leadership and Employee Motivation: Moderating Role of Gender

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Beena Prakash
Keyword(s):  
Transformational Leadership ◽  
Employee Motivation ◽  
Business Environment ◽  
Demand Pull ◽  
Logistics Services ◽  
And Gender ◽  
The Relationship ◽  
Core Issues ◽  
Moderating Role

With the present business environment which is creating a strong demand pull for quality and efficient logistics services, core issues are being gradually removed with time but HR issues are still neglected. Motivation can be the key process of boosting the morale of employees to encourage them to willingly give their best in accomplishing assigned tasks. During growth of any sector, dimensions of leadership can have great impact on employee motivation. This research paper analyzes impact of transformational leadership on employee motivation and moderating role of gender. The result shows significant positive correlation between transformational leadership and employee motivation and gender does moderate the relationship.

Business Location and Success: The Case of Internet Café Business in Indonesia

2004 ◽  
Vol 6 (2) ◽  
pp. 171 ◽  
Author(s):  
Nurul Indarti
Keyword(s):  
Field Research ◽  
Business Environment ◽  
Business Success ◽  
Location Decision ◽  
Location Factors ◽  
Positive Direction ◽  
Independent Variables ◽  
Business Location ◽  
The Relationship ◽  
Commercial Center

This research aims to examine the relationship between business location decision and business success. The case is Internet café business in Indonesia. This research is addressed to answer these main questions: (1) what factors do underlie location decision for an Internet café business?; and (2) does location decision determine success of Internet café business? A field research is conducted to answer these questions.Factor analysis applied to 17 location factors reveals five underlying dimensions of business location decision. They are centrality, business environment, business venue, cost, and labor. Based on responses from 93 Internet cafés in three locations (i.e. Yogyakarta, Surabaya, and Lombok), the author finds that favorable location of business is positively related to business success. More specifically, a regression analysis reveals that availability of utilities, proximity to schools/universities and security affect business success in a positive direction, while proximity to highways, being in commercial center affect in a negative direction. The independent variables explain 23 percent of total variance.

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