Applying and Evaluating Models to Predict Customer Attrition Using Data Mining Techniques

As competition intensifies, retaining customers becomes one of the most serious challenges facing customer service providers. Customer attrition prediction models hold great promise as powerful tools for enhancing customer retention. Several statistical methods have been applied to develop models predicting customer attrition. Yet little research is done on the relative performance of models developed by different methods. The lack of knowledge about the performance of various prediction models is more pronounced due to the nonlinear nature of the combined causes of attrition (such as switching to another provider or canceling a service). The development of data mining techniques has made the comparison of prediction power of different models more efficient and easier. In this article we demonstrate how to use data mining techniques and software to fit and compare different customer attrition prediction models, using data from a major telecom service provider

Temperature differences are associated with malignancy on lung lesions: a clinical study

BACKGROUND: Although new endoscopic techniques can enhance the ability to detect a suspicious lung lesion, the primary diagnosis still depends on subjective visual assessment. We evaluated whether thermal heterogeneity of solid tumors, in bronchial epithelium, constitutes an additional marker for the diagnosis of benign and malignant lesions. METHODS: A new method, developed in our institute, is introduced in order to detect temperature in human pulmonary epithelium, in vivo. This method is based on a thermography catheter, which passes the biopsy channel of the fiber optic bronchoscope. We calculated the temperature differences (ΔT) between the lesion and a normal bronchial epithelium area on 22 lesions of 20 subjects, 50 – 65 years old. RESULTS: Eleven lesions were benign and 11 were malignant, according to the biopsy histology followed the thermography procedure. We found significant differences of ÄT between patients with benign and malignant tumor (0.71 ± 0.6 vs. 1.23 ± 0.4°C, p < 0.05). Logistic regression analysis showed that 1-Celsius degree differences between normal tissue and suspicious lesion six-fold the probability of malignancy (odds ratio = 6.18, 95% CI 0.89 – 42.7). Also, ΔT values greater than 1.05°C, constitutes a crucial point for the discrimination of malignancy, in bronchial epithelium, with sensitivity (64%) and specificity (91%). CONCLUSION: These findings suggest that the calculated ΔT between normal tissue and a neoplastic area could be a useful criterion for the diagnosis of malignancy in tumors of lung lesions

From orthogonal link to phase vortex in generalized dynamical Hopf insulators

In the creation of Hopf topological matters, the old paradigm is to conceive the Hopf invariant first, and then display its intuitive topology through links. Here we brush aside this effort and put forward a new recipe for unraveling the quenched two-dimensional (2D) two-band Chern insulators under a parallel quench protocol, which implies that the quench quantities with different momentum k are parallel or antiparallel to each other. We find that whether the dynamical Hopf invariant exists or not, the links in (2+1)D space always keep their standard shape even for topological initial states, and trace out the trajectories of phase vortices. The linking number is exactly equal to the difference between pre- and post-quench Chern numbers regardless of the construction of homotopy groups. We employ two concrete examples to illustrate these results, highlighting the polarity reversal at fixed points

Effects of seasonal changes on T-helper 1/ T-helper 2 immune balance and eczema onset in rats

Objective: To explore the impact of seasonal changes on the T-helper 1 (Th1)/T-helper 2 (Th2) immune balance of the body in order to elucidate the internal immunological mechanisms underlying seasonal eczema. Methods: Eighty 4-week-old male Sprague–Dawley rats were divided into 5 season groups kept in corresponding season simulation environments, and subdivided into the model group and the control group. The Eczema Area and Severity Index (EASI) and scratching frequency were evaluated. The serum levels of immunoglobulin E (IgE), interleukin-2 (IL-2), interleukin-12 (IL-12), and interferon-gamma (IFN-γ), interleukin-4 (IL-4), interleukin-25 (IL-25), and interleukin-31 (IL-31), and melatonin (MT), as well as the MT receptor (MTR) levels in the spleen, were detected by enzyme-linked immunosorbent assay analysis. The mRNA expression levels of T-bet and GATA3 were detected by quantitative real-time polymerase chain reaction. Results: EASI scores and the scratching frequency of the model group were higher in the long summer than in the other 4 seasons. Meanwhile, the serum levels of IgE and the Th2 cytokines IL-4, IL-25, and IL-31, as well as GATA3 mRNA expression levels, were high during the long summer in both groups. However, the serum levels of the Th1 cytokines IL-2, IL-12, and IFN-γ, as well as MT, MTR, and T-bet mRNA levels, were lower during the long summer. In all 5 seasonal groups (spring, summer, long summer, autumn, and winter), the levels of all immune factors, especially IL-4 and IL-31, were higher in the model group than those in the control group, while the concentrations of MT and MTR were lower. Conclusion: Under long light, hot, and humid conditions in the long summer, the body is more likely to suffer from Th2-dominated immune imbalance. This is the internal mechanism behind the high incidence and severity of eczema during the long summer. MT and MTR play a key role in the seasonal onset of eczema

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. We developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (percentage of upper tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470–2819 mm year−1). Among these sites, we evaluated the PlanetScope-derived deciduousness against corresponding measures derived from WorldView-2 (n = 9 sites) and local phenocams (n = 9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90 m × 90 m) with r2 = 0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r2 ranging from 0.62 to 0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change

Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations

In temperate forests, autumn leaf phenology signals the end of leaf growing season and shows large variability across tree-crowns, which importantly mediates photosynthetic seasonality, hydrological regulation, and nutrient cycling of forest ecosystems. However, critical challenges remain with the monitoring of autumn leaf phenology at the tree-crown scale due to the lack of spatially explicit information for individual tree-crowns and high (spatial and temporal) resolution observations with nadir view. Recent availability of the PlanetScope constellation with a 3 m spatial resolution and near-daily nadir view coverage might help address these observational challenges, but remains underexplored. Here we developed an integration of PlanetScope with drone observations for improved monitoring of crown-scale autumn leaf phenology in a temperate forest in Northeast China. This integration includes: 1) visual identification of individual tree-crowns (and species) from drone observations; 2) extraction of time series of PlanetScope vegetation indices (VIs) for each identified tree-crown; 3) derivation of three metrics of autumn leaf phenology from the extracted VI time series, including the start of fall (SOF), middle of fall (MOF), and end of fall (EOF); and 4) accuracy assessments of the PlanetScope-derived phenology metrics with reference from local phenocams. Our results show that (1) the PlanetScopedrone integration captures large inter-crown phenological variations, with a range of 28 days, 25 days, and 30 days for SOF, MOF, and EOF, respectively, (2) the extracted crown-level phenology metrics strongly agree with those derived from local phenocams, with a root-mean-square-error (RMSE) of 4.1 days, 3.0 days and 5.4 days for SOF, MOF, and EOF, respectively, and (3) PlanetScope maps large variations in autumn leaf phenology over the entire forest landscape with spatially explicit information. These results demonstrate the ability of our proposed method in monitoring the large spatial heterogeneity of crown-scale autumn leaf phenology in the temperate forest, suggesting the potential of using high-resolution satellites to advance crown-scale phenology studies over large geographical areas