A Novel Requirements Prioritization Approach based on 360 Degree Feedback and Group Recommendation

Requirements' prioritization (RP) is an important activity in software development and a crucial step towards making proper decisions for the software release planning. RP is performed by various categories of stakeholders, such as end-users, customers, developers, designers, managers etc. Numerous different techniques exist to prioritize requirements but a set of problems is present in almost every case: (i) hesitation or even lack of knowledge and inability of some stakeholders to evaluate the priorities of some candidate requirements, and (ii) difficulties for stakeholders in reaching consensus on the final RP. The aim of this paper is to present a novel RP approach that addresses these problems and supports the justification of stakeholders' decisions. We categorize stakeholders in distinct sets according to their role in the software project and we ask them to follow a 360-Degree Feedback (360DF) assessment for calculating stakeholders' weights. The RP approach considers as prioritization criteria the satisfaction/dissatisfaction of stakeholders from offering/not offering software requirements as part of next software release and utilizes techniques from Intuitionistic Fuzzy Sets (IFSs) to represent and handle the stakeholders' hesitation/uncertainty. The RP approach also takes advantage from Recommender Systems (RS) to support stakeholders during the evaluation procedure and to assist them to reach consensus for the final prioritization list. © 2021 ACM

Effectors of Pregorexia and Emesis among Pregnant Women: A Pilot Study

During pregnancy, women tend to improve their lifestyle habits and refine their dietary intake. Quite often, however, these dietary improvements take an unhealthy turn, with orthorexia nervosa (ON) practices being apparent. The aim of the present pilot cross-sectional study was to assess the prevalence of ON tendencies and the incidence of pica and record diet practices in a sample of pregnant women. A total of 157 pregnant women were recruited through private practice gynecologists during the first months of 2021. Nutrition-related practices were recorded, orthorexic tendencies were assessed using the translated and culturally adapted Greek version of the ORTO-15 questionnaire, pica practices were evaluated with a binary question and nausea and emesis during pregnancy (NVP) was evaluated using the translated modified Pregnancy—Unique Quantification of Emesis and Nausea (mPUQE). Only two women reported pica tendencies, with ice and snow being the consumed items. The majority (61.1%) of women reported improving their diet since conception was achieved. Folic acid and iron oral nutrient supplements (ONS) were reportedly consumed by the majority of participants (87.9% and 72.6%, respectively) and 9.6% reported using herbal medicine products. The ORTO-15 score was reduced with tertiary education attainment, ART conception, being in the third trimester of pregnancy, consumption of folic acid and MV supplements and was only increased among women who were at their first pregnancy. The majority of participants experienced severe NVP and the remaining experienced moderate NVP. NVP was associated with lower hemoglobin levels, lack of supplementary iron intake, avoidance of gluten-containing foods, as well as with increased gestational weight gain. The results highlight the need to screen pregnant women for disturbed eating behaviors and nutrition-related problems, in order to ensure a healthy pregnancy outcome. © 2022 by the authors

An Approach Based on Intuitionistic Fuzzy Sets for Considering Stakeholders’ Satisfaction, Dissatisfaction, and Hesitation in Software Features Prioritization

This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders’ rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features’ priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods

Orthorexia nervosa: replication and validation of the ORTO questionnaires translated into Greek in a survey of 848 Greek individuals

Purpose: The present study aimed to validate the ORTO-15 questionnaire for orthorexia nervosa (ON), translated by our group into the Greek language, and replicate the findings of the recently proposed 6-item ORTO-R. Methods: The tool was translated into the Greek language (ORTO-15-GR) using the forward–backward–forward method. A total of 848 adults participated in the validation study by filling in the questionnaires and providing general characteristics (age, gender, educational level, body weight, and height). The internal consistency of the tool was assessed by the omega (ω) coefficient, and confirmatory factor analyses (CFA) examined its factorial structure. Using the original six items of the ORTO-15 tool, a separate CFA model examined the factorial structure of the proposed ORTO-R tool. Furthermore, regression models tested the association of ORTO-R with study variables. Results: For ORTO-15-GR, the omega coefficient was 0.70 and for the ORTO-R 0.65. For the latter, the CFA revealed acceptable goodness-of-fit (standardized factor loadings from 0.36 to 0.64); however, all ORTO-15 models were characterized by a poor fit. In addition, there was a negative association between ORTO-R score and female gender, body mass index (BMI), and having a nutrition-related health problem. Conclusion: The replication of ORTO-R indicates that it is a reliable tool in the field of ON. Therefore, the use of a 6-item questionnaire for ON assessment appears promising in research and clinical settings. © 2022, Hellenic Endocrine Society

Moderators of Food Insecurity and Diet Quality in Pairs of Mothers and Their Children

Research has suggested that maternal diet and characteristics may influence the diet of offspring during childhood. The present cross-sectional study aimed to assess the influence of distinct maternal characteristics and the diet quality of mothers on the prevalence of household food insecurity (FI) and the diet quality of children. A total of 179 mother–child pairs were recruited from two primary schools in the metropolitan area of Thessaloniki. The children were aged between 10 and 12 years old. Diet quality was assessed as the level of adherence to the Mediterranean diet (MD), with the use of the KIDMED for the children and the MedDietScore for the mothers. The household FI and the social and demographic characteristics of the mothers were also recorded, and anthropometric measures of both the mothers and their children were collected. Approximately14 (26.3%) of the pairs reported some degree of FI, with a greater prevalence (64.7%) within single-mother families. Moreover, FI affected the level of maternal MD adherence (p = 0.011). On the other hand, FI was decreased in households with a greater maternal educational level (OR: 0.25; 95% CI: 0.10–0.63) and conjugal family status (OR: 0.15; 95% CI: 0.87–0.52). Maternal adherence to the MD was inversely related to the respective adherence of their offspring (OR: 0.93; 95% CI: 0.86–0.997), suggesting that during periods of financial constraints, maternal diet quality is compromised at the expense of affording a better diet for the minors in the family. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach

A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach

Orthorexia nervosa: replication and validation of the ORTO questionnaires translated into Greek in a survey of 848 Greek individuals

Purpose: The present study aimed to validate the ORTO-15 questionnaire for orthorexia nervosa (ON), translated by our group into the Greek language, and replicate the findings of the recently proposed 6-item ORTO-R. Methods: The tool was translated into the Greek language (ORTO-15-GR) using the forward–backward–forward method. A total of 848 adults participated in the validation study by filling in the questionnaires and providing general characteristics (age, gender, educational level, body weight, and height). The internal consistency of the tool was assessed by the omega (ω) coefficient, and confirmatory factor analyses (CFA) examined its factorial structure. Using the original six items of the ORTO-15 tool, a separate CFA model examined the factorial structure of the proposed ORTO-R tool. Furthermore, regression models tested the association of ORTO-R with study variables. Results: For ORTO-15-GR, the omega coefficient was 0.70 and for the ORTO-R 0.65. For the latter, the CFA revealed acceptable goodness-of-fit (standardized factor loadings from 0.36 to 0.64); however, all ORTO-15 models were characterized by a poor fit. In addition, there was a negative association between ORTO-R score and female gender, body mass index (BMI), and having a nutrition-related health problem. Conclusion: The replication of ORTO-R indicates that it is a reliable tool in the field of ON. Therefore, the use of a 6-item questionnaire for ON assessment appears promising in research and clinical settings