Customer relationship management maturity model (CRM3): A model for stepwise implementation

Being multifaceted process, implementing customer relationship management (CRM) project has a high risk and uncertainty that must be reduced using planning to get the desirable benefits. As a matter of fact, existing and optimal position must be determined to reduce the gap between them via suitable investment. To identify this gap as well as the way to higher and optimal condition, maturity model can be used. Relying on extended literature, the present paper reviews the existing models and then develops a model for measuring CRM maturity based on CRM critical success factors, CMMI levels and RADAR logic

Effect of Fiber Angel of Unidirectional Longitude Fiber on Transverse Strength of Pultruded Polyester Rod

In this research by using a new technique which is named, Rotational Pultrusion, angular entrance of uni-directional (UD) fibers directly into the process was successfully reached. E-glass/polyester rods’ samples pultruded with different fiber angles and testing them with three points bending. The test showed that with an increase in UD fiber angle, toward pultruded rod direction, finally the modulus and strength on transverse direction were increased

Existence of Solutions for a System of Integral Equations Using a Generalization of Darbo’s Fixed Point Theorem

In this paper, an extension of Darbo’s fixed point theorem via θ -F-contractions in a Banach space has been presented. Measure of noncompactness approach is the main tool in the presentation of our proofs. As an application, we study the existence of solutions for a system of integral equations. Finally, we present a concrete example to support the effectiveness of our results.The authors are grateful to the Basque Government by the support of this work through Grant IT1207-19

Insights into the Wnt Signaling Pathway Evolution

Animals’ Wnt signaling pathways are highly preserved signal transduction pathways, which play a crucial role in embryogenesis and adult tissue homeostasis. This chapter reviews the three major Wnt pathways, focusing on some critical proteins in the Wnt/β-catenin path in terms of their evolution and role in homeostasis. Wnt proteins act as a gateway between extracellular, cytoplasmic, and nuclear components to transmit signaling pathways. The Frizzled (FZD) family as G-protein-coupled receptors activates the signaling pathways by binding to Wnt ligands. LRP5/6, members of the family of low-density lipoprotein receptors (LDLR), associate with FZD receptor and Wnt ligands as co-receptors to initiate the Wnt/β-catenin pathway. The Wnt/β-catenin pathway is regulated by antagonists such as the Dickkopf and secreted Frizzled-related protein (SFRP) families

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows

Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Background Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD. Methods Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers. Functional variables were derived from registration of TLC and RV images, e.g. functional small airways disease (fSAD%). Structural variables were assessed at TLC images, e.g. emphysema and airway wall thickness and diameter. We employed an unsupervised method for clustering. Results Four clusters were identified. Cluster 1 had relatively normal airway structures; Cluster 2 had an increase of fSAD% and wall thickness; Cluster 3 exhibited a further increase of fSAD% but a decrease of wall thickness and airway diameter; Cluster 4 had a significant increase of fSAD% and emphysema. Clinically, Cluster 1 showed normal FEV1/FVC and low exacerbations. Cluster 4 showed relatively low FEV1/FVC and high exacerbations. While Cluster 2 and Cluster 3 showed similar exacerbations, Cluster 2 had the highest BMI among all clusters. Conclusions Association of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

Background: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping. Methods: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration. Results: We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema. Conclusions: QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.NIH [U01-HL114494, R01-HL112986, S10-RR022421]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1D1A1B03034157]; Korea Ministry of Environment (MOE) [RE201806039]; NIH/NHLBI [HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Projection of Future Meteorological Droughts in Lake Urmia Basin, Iran

Future changes (2015–2100) in precipitation and meteorological droughts in Lake Urmia Basin were investigated using an average mean ensemble of eight general circulation models (GCMs) with high-resolution datasets in socioeconomic pathway scenarios (SSPs) from the Coupled Model Intercomparison Project (CMIP6). In order to project the drought, the standardized precipitation index (SPI) was calculated. Overall, the results revealed that precipitation in Lake Urmia Basin will decrease by 3.21% and 7.18% in the SSP1-2.6 and SSP5-8.5 scenarios, respectively. The results based on 6-month-timescale SPI indices projected more “Extremely dry” events in SSP5-8.5 scenarios. The frequency of “Extremely dry” months in SSP5-8.5 compared to SSP1-2.6 is expected to increase by 14, 7, 14, 10, 5, 14, and 7 months for the Mahabad, Maragheh, Saqez, Sarab, Tabriz, Takab, and Urmia stations, respectively. In contrast, the frequency of “Extremely wet” months will decline for all stations in Lake Urmia Basin. The results of this study provide useful insight for considering drought prevention measures to be implemented in advance for Lake Urmia Basin, which is currently experiencing various environmental issues