Engagement, Search Goals and Conversion - The Different M-Commerce Path to Conversion

While the use of smartphones is increasing, conversion rates for mobile platforms are still significantly lower than those for traditional e-commerce channels, suggesting that these platforms are characterized by distinct consumption patterns. In this research, using detailed event log-files of an online jewelry retailer, we analyze user engagement and navigation behaviors on both platforms, model search goals and their effect on purchase decisions, and develop a conversion prediction model. Our initial results show that user engagement is significantly higher in PC sessions compared to mobile sessions, although mobile sessions reflect a higher level of user engagement than PC sessions. These results indicate that m-commerce involves more than ensuring mobile-compatibility of websites, and that mobile consumers follow a distinct path to purchase involving distinct search and browsing behaviors. Therefore, analysis of the different types of consumption behaviors is necessary to understand the factors that lead to conversion on mobile e-commerce platforms

M-COMMERCE VS. E-COMMERCE: EXPLORING WEB SESSION BROWSING BEHAVIOR

With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand m-commerce usage behavior. \ \ In this study, we investigate browsing behavior patterns based on the analysis of clickstream data that is recorded in server-side log files. We compare consumers\u27 browsing behaviors in the m-commerce channel against the traditional e-commerce channel. For the comparison, we offer an integrative web usage mining approach, combining visualization graphs, association rules and classification models to analyze the Web server log files of a large Internet retailer in Israel, who introduced m-commerce to its existing e-commerce offerings. \ \ The analysis is expected to reveal typical m-commerce and e-commerce browsing behavior, in terms of session timing and intensity of use and in terms of session navigation patterns. The obtained results will contribute to the emerging research area of m-commerce and can be also used to guide future development of mobile websites and increase their effectiveness. Our preliminary findings are promising. They reveal that browsing behaviors in m-commerce and e-commerce are different

Macular Hole following Intravitreal Bevacizumab Injection in Choroidal Neovascularization Caused by Age-Related Macular Degeneration

This report describes formation of a full-thickness macular hole subsequent to an injection of bevacizumab for the treatment of neovascular AMD. This complication may be caused by focal tractional forces on the retinal surface due to either vitreous incarceration at the injection site or contraction of the choroidal neovascularization membrane. Alternatively, it may be due to a toxic effect of bevacizumab on a previously compromised retina

Fewer Circulating Endothelial Progenitor Cells in Newly Diagnosed Neovascular AMD Patients

Abstract: Objective: Patients with age-related macular degeneration (AMD) exhibit pathologic neovascularization under the retina, with choroidal neovascularization (CNV) suggestive of defective angiogenesis. The endothelial progenitor cells (EPCs) present in the peripheral blood contribute to angiogenesis and vasculogenesis, and their regulation is altered in several vascular disorders. We investigated whether the numbers and functional properties of EPCs may be disordered in newly diagnosed neovascular AMD. Methods Fifteen suitable AMD patients and 10 controls matched for age, risk factors for atherosclerosis and use of medication that could influence the circulating pool of EPCs were studied. Circulating EPCs were assayed by the colony-forming unit (CFU) method. The EPCs' adhesive capacity was studied by evaluating their ability to attach to fibronectin and cultured endothelial cells. Serum levels of vascular endothelial growth factor (VEGF) were studied and correlated with EPC numbers. Results: The patients had significantly fewer circulating EPCs(16.5±2.8) compared to their controls (31±4.6; p=0.0085). The functional properties of both groups' EPCs were similar. Conclusions: The peripheral circulating pool of endothelial stem cells is altered in patients with newly diagnosed neovascular AMD, suggesting that pathologic angiogenesis may result from or influence the regulation of endothelial precursor circulation

Discrete Kinetic Models from Funneled Energy Landscape Simulations

A general method for facilitating the interpretation of computer simulations of protein folding with minimally frustrated energy landscapes is detailed and applied to a designed ankyrin repeat protein (4ANK). In the method, groups of residues are assigned to foldons and these foldons are used to map the conformational space of the protein onto a set of discrete macrobasins. The free energies of the individual macrobasins are then calculated, informing practical kinetic analysis. Two simple assumptions about the universality of the rate for downhill transitions between macrobasins and the natural local connectivity between macrobasins lead to a scheme for predicting overall folding and unfolding rates, generating chevron plots under varying thermodynamic conditions, and inferring dominant kinetic folding pathways. To illustrate the approach, free energies of macrobasins were calculated from biased simulations of a non-additive structure-based model using two structurally motivated foldon definitions at the full and half ankyrin repeat resolutions. The calculated chevrons have features consistent with those measured in stopped flow chemical denaturation experiments. The dominant inferred folding pathway has an “inside-out”, nucleation-propagation like character

Self-report symptom-based endometriosis prediction using machine learning

Abstract Endometriosis is a chronic gynecological condition that affects 5–10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women: women who were diagnosed with endometriosis and women who were not diagnosed. The best performing model had AUC of 0.94, sensitivity of 0.93, and specificity of 0.95. The model is intended to be incorporated into a website as a self-diagnostic tool and is expected to shorten time-to-diagnosis by referring women with a high likelihood of having endometriosis to further examination. We also report the importance and effectiveness of different symptoms in predicting endometriosis

Enterprise Models as Drivers for IT Security Management at Runtime

Abstract. This paper describes how enterprise models can be made suitable for monitoring and controlling IT security at runtime. A holistic modeling method is proposed that extends enterprise models with runtime information, turning them into dashboards for managing security incidents and risks, and supporting decision making at runtime. The requirements of such a modeling method are defined and an existing enterprise modeling language is extended with relevant security concepts that also capture runtime information to satisfy these requirements. Subsequently, the resulting modeling method is evaluated against the previously defined requirements. It is also shown that common metamodeling frameworks are not suitable for implementing a modeling environment that results in suitable IT security dashboards. This leads to suggesting implementation of the modeling environment using the eXecutable Modeling Facility.