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Although 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.

Considering 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.

In 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.

The 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.

Machine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

This paper presents an evaluation of several types of neural networks for object recognition by means of ultrasonic sensors. Initially, in order to obtain information from the ultrasonic signal, a parametric method is proposed and a set of features is extracted from the ultrasonic echo envelope. Then, it is necessary to evaluate how much information is provided for each characteristic obtained. Therefore, it has been necessary to carry out an analysis in order to detect the most relevant features. Results about information provided for each feature are presented by order of preference. Subsequently, using these features extracted from the echo signal, an experimental set‐up has been carried out in order to highlight the capabilities of different types of neural networks with this information. Finally, results obtained from experimental tests are presented, and the pattern recognition capabilities of each neural network type, using the selected features, are shown.

The global fashion industry is growing at a rapid pace and developing nations such as India are emerging as major contributors to the same. In such case, most academics and marketers are interested in the variables that influence fashion shopping. The purpose of this paper is to investigate the influence of consumer demographic and personality characteristics on fashion shopping proneness (FSP) in India.

Data were collected from 561 respondents using mall intercept survey method. Hypothesized relationships were assessed using multiple regression and structural equation modelling.

Traditional view that younger and female consumers are more fashion prone than older and male consumers is validated. However, demographics accounted for only 9 per cent of the variance in FSP while personality characteristics accounted for 46 per cent. Being agreeable, extroverted, open minded, and stable are all positively associated with fashion shopping.

The study finds both personality dimensions and consumer demographics to influence FSP. As a limitation, the authors do not probe deep into the why and how of the mentioned relationships between personality and fashion buying.

With respect to demographics, managers could target young females as the primary segment for fashion clothing but cannot ignore young males and older females. With respect to personality, managers can appeal to agreeable, extroverted, open-minded personalities by linking novelty, fun, relaxation, and recreation with fashion buying.

This is one of the first attempts that simultaneously investigates the effects of demographic and personality characteristics on fashion shopping behaviour in India.

The aim of this study is to provide a holistic analysis of all possible maritime business logistics processes related to import and export shipments in a fuzzy environment through a case study of a maritime logistics company based on the as-is and to-be models within business process management (BPM).

The analyses considered the following perspectives: (i) in the stage of the process identification, the definition of the problem was carried out; (ii) in the stage of the process discovery, ocean department was divided into ocean export/import operation departments; ocean export/import operation were divided into freight collect/prepaid operation processes; ocean export/import logistics activity groups were broken down into sub-activities for freight collect/prepaid operation; the logistics activity groups and their sub-activities were defined; each sub-activity as either operation or documentation process group was classified; the durations of sub-activities were evaluated by decision-makers (DMs) as fuzzy sets (FSs); the monthly total jobs activities were estimated by DMs as FSs; the applied to monthly jobs activities of total shipments were estimated by DMs as FSs; the durations of each sub-activities were aggregated; the duration of the logistics activity groups and the sub-activities for per job were calculated; the cumulative workload of logistics activity groups and sub-activities were calculated; the duration of sub-activities for per job as operation or documentation departments were calculated, (iii) in the stage of the process analysis, cumulative ocean export/import workload as operation or documentation for freight collect/prepaid were calculated; duration of activity groups and sub-activities for per job as operation or documentation were calculated; cumulative workload activity groups and sub-activities as operation or documentation were calculated, (iv) in the stage of the process redesign, cumulative workload, process cycle time as operation and documentation group and required labor force were calculated; the process cycle time of the theoretical, the as-is model and the to-be model were calculated: (i) the theoretical minimum process cycle time without resource were calculated by the critical path method (CPM), (ii) the process cycle time of the as-is model perspective with the 1 person resource constraint and (iii) the process cycle time of the to-be model perspective with the 2-person resource constraint were calculated by the resource constrained project scheduling problem (RCPSP) method.

The methodology for analyzing the ocean department operation process was successfully implemented in a real-life case study. It is observed that the results of the to-be model can be applicable for the company. The BPM-proposed methodology is applicable for the maritime logistics industry in the present study; however, it can be applied to other companies in maritime logistics as well as other industries.

This study contributes to research using BPM methodology in maritime logistics. This is the first study the logistics process analyses were carried out in terms of including all operation processes for a company. All processes were analyzed by using BPM methodology in maritime logistics. This study demonstrated the application of the BPM as-is and to-be models to maritime logistics. The as-is and the to-be models of the BPM methodology were applied in maritime logistics.

This methodology applied in this study can enable organizations operating in the time-urgent maritime logistics sector to manage their logistics processes more efficiently, increase customer satisfaction, reduce the risks of customer loss due to poor operational performance and increase profits in the long term. Through the use of these methodologies utilizing FSs, the CPM and the RCPSP methods, this study is expected to make contributions to the BPM literature and provide original insights into the field. Furthermore, this study will undertake a comprehensive analysis of maritime logistics with respect to BPM to deliver noteworthy contributions to the maritime logistics literature and provide original perspectives into the field.

Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM.

In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework.

The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details.

A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm.

This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.

During the past decades, several researchers have introduced devices that use sonar systems to detect and/or to determine the object location or to measure the distance to an object using reflected sound waves. The purpose of this paper is to use sonar sensor with textile structure and to test it for detection of objects.

In this study, a sonar system based on intelligent textiles approach for detection of objects has been developed. In order to do this, ultrasonic sensor has been integrated to textile structures by using conductive yarns. Furthermore, an electronic circuit has been designed; PIC 16F877 microcontroller unit has been used to convert the measured signal to meaningful data and to assess the data. The algorithm enabling the objects detection has also been developed. Finally, smart textile structure integrated with ultrasonic sensor has been tested for detection of objects.

Beam shape is presented related to identified object and compared with the actual one given in sensor's datasheet in order to test the efficiency of the proposed method of detection. The achieved results showed that the determined beam pattern matches with the actual one given in its datasheet. Therefore, it can be concluded that the integration of sensor was successful.

This is the first time in the literature that a sonar sensor was integrated into textile structure and tested for detection of objects.

The purpose of this paper is to recognise the role of implicit and explicit attitudes on organic wine purchase intention and to segment consumers using these variables.

The authors conducted a two-part Web survey (n = 690) in Spain: an Implicit Association Test followed by a questionnaire on explicit attitudes, purchase intention and demographic data. Validity and reliability of these attitudes are contrasted using confirmatory factor analysis, attitude relationships with purchase intention using multiple linear regression analysis, and segments using k-means cluster and discriminant analyses.

The authors improve the measurement of explicit attitudes explaining organic wine purchase intention. Only attitudes towards intrinsic attributes and arousal feelings significantly explain purchase intention. Two attitudinal segments are detected, one showing moderate purchase intention with high explicit attitudinal levels and high consumption of organic wine and the other showing low levels of purchase intention and explicit attitudes, consuming mainly conventional wines. Neither segment shows any relevant differences in implicit attitudes.

The analysis offers information on attitudes that contribute to explain Spanish consumer purchase intention in a wine sector notable for focusing more on making quality products than by knowing its market.

The authors offer deeper understanding of the influence of attitudes on organic wine purchase intention. This paper also presents an attitudinal segmentation of consumers.

This paper aims to estimate the global income distribution during the nineties using limited information. In a first stage, we obtain national income distributions considering a model with two parameters. In particular, we propose to use the so-called Lamé distributions, which are curved versions of the Sigh-Maddala and Dagum distributions. The main feature of this family is that they represent parsimonious models which can fit income data adequately with just two parameters and whose Lorenz curves are characterized by only one parameter. In a second stage, global and regional distributions are derived from a finite mixture of these families using population shares. We test the validity of the model, comparing it with other two-parameter families. Our estimates of different inequality measures suggest that global inequality presents a decreasing pattern mainly driven by the fall of the differences across countries during the course of the study period that offsets the increase in disparities within countries.

The purpose of this research has been to investigate whether burnout and eating behaviour traits were associated with food intake.

Participants (n=109) 78 per cent female, mean age 39 years, were recruited from various occupations within a UK university to complete an on-line survey. Dietary habits were measured using Food Frequency Questionnaire (FFQ), burnout using the Maslach Burnout Inventory (MBI) and eating behaviour traits using the Three Factor Eating Questionnaire (TFEQ) R18.

Principal component analyses of FFQ responses revealed four dietary patterns: fast/junk food (+chicken and low fruit/vegetables); meat/fish; dairy/grains; beans/nuts. Dietary patterns were examined using multiple regression analysis as outcome variables with age, gender, burnout and eating behaviour traits as explanatory variables. More frequent consumption of “junk/fast food” was associated with lower TFEQ-Cognitive Restraint, higher TFEQ-Uncontrolled Eating (UE), lower MBI-Emotional Exhaustion and higher MBI-Depersonalisation. More frequent consumption of beans/nuts was associated with higher TFEQ-UE and higher MBI-Emotional Exhaustion. Models for meat/fish and grains/dairy dietary patterns were not significant.

Burnout may need to be considered to reduce junk food consumption in higher education employees. Causality between burnout, eating behaviour traits and food consumption requires further investigation on larger samples.

This appears to be the first study to have explored associations between burnout, eating behaviour traits and dietary patterns.

The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.