Comparing hazard models for the growth failure of children in Iran

One of the statistical methods deployed in medical sciences to investigate time to event data is the survival analysis. This study, comparing efficiency of some parametric and semiparametric survival models, aims at investigating the effect of demographic and socio-economic factors on the growth failure of children below 2 years of age in Iran. The survival models including exponential, Weibull, log-logistic and log-normal models were compared to proportional hazards and extended Cox models by Akaike Information Criterion and variability of the estimated parameters. Based on the results, the log-normal model is recommended for analyzing the growth failure data of children in Iran. Furthermore, it is suggested that female children, children born to illiterate mothers and children born in larger households receive more attention in terms of growth failure.     

Fingolimod versus natalizumab in patients with relapsing remitting multiple sclerosis: a cost-effectiveness and cost-utility study in Iran

Aims: Multiple sclerosis (MS) is a chronic, autoimmune, and inflammatory disease. If the first-line medicines are not effective enough, specialists will prescribe second-line medicines, such as natalizumab and fingolimod. This study aimed to compare the cost-effectiveness and cost-utility of fingolimod with those of natalizumab in patients with relapsing-remitting multiple sclerosis (RRMS) in Iran, Fars province in 2016.

Materials and methods: This study was a cost-effectiveness and cost-utility study in which a Markov model was used. The study used the census method to evaluate 81 patients with MS in Iran, Fars province who were being treated with fingolimod and natalizumab. In this study, costs were collected from the societal perspective, and the outcomes were the mean of relapse avoided rate and QALY. The cost data collection form, Kurtzke Expanded Disability Status Scale, and EQ-5D-3L questionnaire were used to collect the required data.

Results: The results showed that, compared to natalizumab, patients who used fingolimod had decreased costs (58,087 vs 201,707), increased QALYs (8.09 vs 7.37), and a better relapse avoided rate (6.27 vs 5.83) per patient over the lifetime. The results of the sensitivity analysis showed that the results of the study were robust. Also, the results of the scatter plots showed that fingolimod was more cost-effective based on the QALY and relapse avoided rate in 62% and 56%, respectively, of the simulations for the thresholds below $15,657 for the studied patients.

Conclusions: According to the results of this study, the cost-effectiveness and cost-utility of fingolimod were higher than those of natalizumab. Therefore, it is recommended that treatment with fingolimod be the first priority of second-line treatment for MS patients, and policy-makers and health managers are encouraged to make efforts in order to increase insurance coverage and reduce the out-of-pocket payments of these patients.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

The customer experience ecosystem in two cultural contexts

This study focuses on the development of a customer experience ecosystem during a journey which is embedded in meso- and macro-layers. Using the critical incident technique, the author collected in-depth interview data from bank customers in Switzerland and Iran to empirically study this ecosystem, including customer–company interaction in the micro-layer and social context of the meso-layer. Moreover, in a macro-layer analysis, the Hofstede cultural dimension was employed to show the role of cultural context in this ecosystem. The findings indicate that customer experience in the pre-encounter stage is mostly shaped by customer past experience and social context rather than company touchpoint. The importance of these factors is different in the two cultural contexts. Although customer experience in the encounter stage is mainly the result of customer and company interactions, other people have a role in this stage and cultural differences between the two countries largely explain these differences. Moreover, in the post-encounter stage, customers in different cultural contexts use various factors to evaluate their experiences and the effects on their emotional and behavioral responses. The findings provide key managerial implications for national and international companies with respect to the role of multiple layers in customer experience management.     

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.

     

Optimal Design and Operation of a Hydropower Reservoir Plant Using a WEAP-Based Simulation–Optimization Approach

Water Resources Management - Optimal design and operation of a hydropower reservoir is a complex optimization problem in terms of formulation and solution. In this study, a...     

A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

Water Resources Management - In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of...     

Investigation of the efficiency of a tubular continuous-flow photoreactor with supported titanium dioxide nanoparticles in the removal of 4-nitrophenol: operational parameters, kinetics analysis and mineralization studies

In this study, to investigate the application of heterogeneous photocatalysis in the removal of organic contaminants from aqueous media a novel tubular continuous-flow photoreactor with supported TiO2-P25 on glass plates was designed and constructed. The photoreactor comprises six quartz tubes and a UV lamp which was placed in the center of the quartz tubes. 4-nitrophenol (4-NP) as a most refractory pollutant was chosen as a probe pollutant to examine the photoreactor efficiency for environmental cleaning applications. Results of experiments show that the removal efficiency of 4-NP in this photoreactor is a function of photoreactor length, gas and liquid flow rates and 4-NP initial concentration. Kinetics analysis indicates that degradation of 4-NP in continuous-mode can be modeled with the Langmuir-Hinshelwood (L-H) model (k(L-H) = 1.5 mg L(-1) min(-1), K(ads) = 0.11 mg(-1) L). A design equation was obtained with a combination of L-H modified equation and tubular reactor design equation. This equation can be used for estimation of 4-NP concentration in different photoreactor lengths under various operational parameters. Mineralization study was followed through total organic carbon (TOC) analysis and measurement of nitrite and nitrate as final degradation products.     

A Multi-Objective Risk-Based Game Theoretic Approach to Reservoir Operation Policy in Potential Future Drought Condition

In this paper, by using the concept of Conditional Value at Risk (CVaR), a Leader-Follower game (LFG) based multi-objective optimization model is developed to determine the optimum 12-month operation policy of a reservoir in potential future dry periods. The minimization of CVaRs of storage loss and agricultural and environmental deficits along with maximization of planned allocation to agricultural sector are considered as leader’s objectives, while the followers try to maximize their share of water rights using Nash bargaining (NB) method. This framework is then used to model the operation policy of Dorudzan basin in Fars province, southwestern Iran. Water demand and daily climate data in the period of 2003 to 2015 for this basin, as well as future projections from fifteen IPCC-AR4 global circulation models (GCMs) for 2018–2030 under A2, B1 and A1B emission scenarios are considered to evaluate future dam operation policies. Future projections are downscaled using the LARS-WG model, which then feeds the HMETS watershed model to simulate the corresponding reservoir inflow time-series. Thereafter, three-hundred 12-month rainfall, evaporation and inflow time series with least inflow volume are used as input for the optimization model, which is solved using NSGA-II and GA algorithms. The results show while the model can determine the operation policy that keeps the associated risks in the acceptable range, it can satisfy the followers demands with respect to the available resources. The results also show that the agricultural sector of the study area can be hugely affected by potential future droughts.