Results 91 to 100 of about 1,078,545 (255)

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

Loyalty Programme Applications in Indian Service Industry [PDF]

open access: yes
Retaining all customers would not be a good idea for any business. In contrast, allowing the profitable customers to leave would be an even worse idea. Consequently the real solution rests in knowing the value of each customer and then focusing loyalty ...
Vyas Preeta H, Sahoo Debajani
core  

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

open access: yesAdvanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran   +6 more
wiley   +1 more source

DETERMINANTS OF CUSTOMER LOYALTY AND PROPOSING A CUSTOMER LOYALTY MODEL FOR THE BANKING SECTOR OF PAKISTAN [PDF]

open access: yes
It is always costly to attract new customers, so the managers always try to find ways to retain their current customers and concentrate on different factors which enhances the customer loyalty among the customers of the organizations.
Afsar BILAL
core  

Annual report to the Behavioral Health Partnership Oversight Council. 2010: Dec.

open access: yes, 2012
Annual; Began with 2009?; "Pursuant to CGS Section 17a-22m ..."--Letter of transmittal (publisher's Web site, viewed Apr. 1, 2011)
Connecticut Behavioral Health Partnership.
core   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

open access: yesAdvanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi   +3 more
wiley   +1 more source

A review of brand-loyalty measures in marketing. [PDF]

open access: yes
Brand loyalty represents an important asset to the firm. While considerable agreement exists on its conceptual definition, no unified approach to operationalize the concept has yet emerged in the marketing literature. We provide a conceptual framework to
Mellens, M   +2 more
core  

Antecedent Behavioral Loyalty: Study On Surabaya University Cooperative

open access: yes
The study of cooperatives has developed both nationally and internationally and is still being discussed. All this, because the impact is believed to be quite large, not only for cooperative members but also for society and the nation. This study was
Susilo, Agus   +2 more
core   +1 more source

The influence of brand experience on brand loyalty in the electronic commerce sector: the mediating effect of brand association and brand trust

open access: yesCogent Business & Management
Businesses are increasingly focusing on brand experience due to its dual role in directly creating customer value and shaping customers’ perceptions of brand equity.
Vo Minh Sang, Mai Chi Cuong
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

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