Applying FCM to Predict the Behaviour of Loyal Customers in the Mobile Telecommunications Industry

Using empirical data from the Kuwaiti mobile telecommunications sector, this study models a fuzzy cognitive map (FCM) to investigate the reciprocal effects of customer loyalty and its antecedents in an emerging market context. This study investigates the effect of perceived service quality, perceived service value and brand equity on customer loyalty and the simultaneous analysis of the reverse causality of these variables. Data pertaining to 350 subscribers were analysed. According to the results, the model reaches the equilibrium when brand equity and customer loyalty are increased and reach an optimal level. Based on these findings, the authors provide implications for managers in the mobile telecom industry

Machine Learning for the identification of phase-transitions in interacting agent-based systems

Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM in its mean-field limit, using a smaller number of variables than traditional closed-form models. To this end, we use the manifold learning algorithm Diffusion Maps to identify a parsimonious set of data-driven latent variables, and show that they are in one-to-one correspondence with the expected theoretical order parameter of the ABM. We then utilize a deep learning framework to obtain a conformal reparametrization of the data-driven coordinates that facilitates, in our example, the identification of a single parameter-dependent ODE in these coordinates. We identify this ODE through a residual neural network inspired by a numerical integration scheme (forward Euler). We then use the identified ODE -- enabled through an odd symmetry transformation -- to construct the bifurcation diagram exhibiting the phase transition.Comment: 14 pages, 9 Figure

Integrating Multiple Sources of Knowledge for the Intelligent Detection of Anomalous Sensory Data in a Mobile Robot

For service robots to expand in everyday scenarios they must be able to identify and manage abnormal situations intelligently. In this paper we work at a basic sensor level, by dealing with raw data produced by diverse devices subjected to some negative circumstances such as adverse environmental conditions or difficult to perceive objects. We have implemented a probabilistic Bayesian inference process for deducing whether the sensors are working nominally or not, which abnormal situation occurs, and even to correct their data. Our inference system works by integrating in a rigorous and homogeneous mathematical framework multiple sources and modalities of knowledge: human expert, external information systems, application-specific and temporal. The results on a real service robot navigating in a structured mixed indoor-outdoor environment demonstrate good detection capabilities and set a promising basis for improving robustness and safety in many common service tasks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A multi-center, real-life experience on liquid biopsy practice for EGFR testing in non-small cell lung cancer (NSCLC) patients

Background: circulating tumor DNA (ctDNA) is a source of tumor genetic material for EGFR testing in NSCLC. Real-word data about liquid biopsy (LB) clinical practice are lacking. The aim of the study was to describe the LB practice for EGFR detection in North Eastern Italy. Methods: we conducted a multi-regional survey on ctDNA testing practices in lung cancer patients. Results: Median time from blood collection to plasma separation was 50 min (20\u2013120 min), median time from plasma extraction to ctDNA analysis was 24 h (30 min\u20135 days) and median turnaround time was 24 h (6 h\u20135 days). Four hundred and seventy five patients and 654 samples were tested. One hundred and ninety-two patients were tested at diagnosis, with 16% EGFR mutation rate. Among the 283 patients tested at disease progression, 35% were T790M+. Main differences in LB results between 2017 and 2018 were the number of LBs performed for each patient at disease progression (2.88 vs. 1.2, respectively) and the percentage of T790M+ patients (61% vs. 26%)

Prospective, Multicenter Phase II Trial of Non-Pegylated Liposomal Doxorubicin Combined with Ifosfamide in First-Line Treatment of Advanced/Metastatic Soft Tissue Sarcomas

Doxorubicin is a widely used anticancer agent as a first-line treatment for various tumor types, including sarcomas. Its use is hampered by adverse events, among which is the risk of dose dependence. The potential cardiotoxicity, which increases with higher doses, poses a significant challenge to its safe and effective application. To try to overcome these undesired effects, encapsulation of doxorubicin in liposomes has been proposed. Caelyx and Myocet are different formulations of pegylated (PLD) and non-pegylated liposomal doxorubicin (NPLD), respectively. Both PLD and NPLD have shown similar activity compared with free drugs but with reduced cardiotoxicity. While the hand–foot syndrome exhibits a high occurrence among patients treated with PLD, its frequency is notably reduced in those receiving NPLD. In this prospective, multicenter, one-stage, single-arm phase II trial, we assessed the combination of NPLD and ifosfamide as first-line treatment for advanced/metastatic soft tissue sarcoma (STS). Patients received six cycles of NPLD (50 mg/m2) on day 1 along with ifosfamide (3000 mg/m2 on days 1, 2, and 3 with equidose MESNA) administered every 3 weeks. The overall response rate, yielding 40% (95% CI: 0.29–0.51), resulted in statistical significance; the disease control rate stood at 81% (95% CI: 0.73—0.90), while only 16% (95% CI: 0.08–0.24) of patients experienced a progressive disease. These findings indicate that the combination of NPLD and ifosfamide yields a statistically significant response rate in advanced/metastatic STS with limited toxicity

P-110 Hepatocellular carcinoma in elderly patients: Final results of the Italian cohort of GIDEON study

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Health status, mental health and air quality: evidence from pensioners in Europe

Environmental quality is an important determinant of individuals’ well-being and one of the main concerns of the governments is the improvement on air quality and the protection of public health. This is especially the case of sensitive demographic groups, such as the old aged people. However, the question this study attempts to answer is how do individuals value the effects on the environment. The study explores the effects of old and early public pension schemes, as well as the impact of air pollution on health status of retired citizens. The empirical analysis relies on detailed micro-level data derived from the Survey of Health, Ageing and Retirement in Europe (SHARE). As proxies for health, we use the general health status and the Eurod mental health indicator. We examine two air pollutants: the sulphur dioxide (SO2) and ground-level ozone (O3). Next, we calculate the marginal willingness-to-pay (MWTP) which shows how much the people are willing to pay for improvement in air quality. We apply various quantitative techniques and approaches, including the fixed effects ordinary least squares (OLS) and the fixed effects instrumental variables (IV) approach. The last approach is applied to reduce the endogeneity problem coming from possible reverse causality between the air pollution, pensions and the health outcomes. For robustness check, we apply also a structural equation modelling (SEM) which is proper when the outcomes are latent variables. Based on our favoured IV estimates and the health status, we find that the MWTP values for one unit decrease in SO2 and O3 are respectively €221 and €88 per year. The respective MWTP values using the Eurod measure are €155 and €68. Overall, improvement of health status implies reduction in health expenditures, and in previous literature, ageing has been traditionally considered the most important determinant. However, this study shows that health lifestyle and socio-economic status, such as education and marital status, are more important, and furthermore, air pollution cannot be ignored in the agenda of policy makers

Worthy to Lose Some Money for Better Air Quality: Applications of Bayesian Networks on the Causal Effect of Income and Air Pollution on Life Satisfaction in Switzerland

One important determinant of well-being is the environmental quality. Many countries apply environmental regulations, reforms and policies for its improvement. However, the question is how the people value the environment, including the air quality. This study examines the association between air pollution and life satisfaction using the Swiss Household Panel survey over the years 2000–2013. We follow a Bayesian network (BN) strategy to estimate the causal effect of the income and air pollution on life satisfaction. We look at five main air pollutants: the ground-level ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter of 10 micrometres (PM10). Then, we calculate the individuals’ marginal willingness to pay (MWTP) of reducing air pollution that aims to improve their life satisfaction. Beside the BN model, we take advantage of the panel structure of our data and we follow two approaches as robustness check. This includes the adapted probit fixed effects and the generalised methods of moments system. Our findings show that O3 and PM10 present the highest MWTP values ranging between $8000 and$12,000, followed by the remained air pollutants with MWTP extending between $2000 and$6500. Applying the BNs, we find that the causal effect of income on life satisfaction is substantially increased. We also show the causal effects of air pollutants remain almost the same, leading to lower values of willingness to pay

Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined