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Background and Aim: Pediculus humanus capitis has been prevalent throughout the world, especially in developing countries among elementary students and societies with a weak socio-economic status. This study aimed to forecast head lice (Pediculidae: P. capitis) infestation incidence hotspots based on spatial correlation analysis in Ardabil Province, Northwest Iran. Materials and Methods: In this retrospective analytical study, all cases of head lice infestations who were confirmed by Centers for Disease Control office have been studied from 2016 to 2018. Head lice infestation incidence hotspots in the province should be detected based on general G statistics in ArcMap GIS10.4.1. Furthermore, MaxEnt.3.3.3 model was used for modeling the high-risk areas. Results: The prevalence rate of pediculosis was 14.90/100,000 populations. The general G statistics revealed that the head lice infestation in this study area has a high cluster pattern. The analysis showed that the Parsabad and Germi counties were identified as a head lice infestation incidence hotspots. Statistical and spatial analyses of head lice infestation incidence showed a significant positive correlation with head lice infestation incidence hotspots and the altitudes (15-500 m), annual temperature range (14-16.5°C), and slope and average diurnal temperature (12-18°C). Conclusion: The results of this study showed that the most ecologically suitable areas of head lice occurrence were identified in two hotspots (Parsabad and Germi) in the Northern areas of Ardabil Province (Parsabad and Germi counties); in the borderline of Iran and the Republic of Azerbaijan.

PurposeAlthough Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.Design/methodology/approachConsidering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.FindingsIn contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.Practical implicationsThe results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.Originality/valueMachine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

This paper investigates how selecting appropriate forecasting parameters could be useful in reducing ordering variances (i.e. bullwhip effect) moving up stream a four-level supply chain. We examined 40 different scenarios for each echelon of supply chain in order to find governing rules in determination the best forecasting parameters. Relying on extensive spreadsheet computation we found some interesting results which need more investigations to be accepted as a general rule for all forecasting techniques. The results shown in tables and charts demonstrate that increasing number of periods used for initial average calculating in smoothing formula (T) decreases ordering variances. Moreover, increasing weighting factor () in exponential smoothing formula results in increasing variances of orders; that is, it results in intensified bullwhip effect. Regarding these two findings, we will show that simultaneous increase of forecasting parameters (T,) will alleviate ordering variances or bullwhip effect. The most important limitation of the research is that there are some informal local producers whose levels of production could not be exactly determined. We had to simply add their production variances to the demand variance of each echelon. However, designing computerized systems for archiving data during the time will be useful for finding the variances pattern. This investigation was carried out on the real supply chain of a dairy company (Mahghol Co.) and the results made good progress in improving supply chain management and inventory policy.

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