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This study aims to revise the weights in the interrelationship matrix of the house of quality.

After determining customer requirements (CRs) and product design characteristics (PDCs), a house of quality (HoQ) has been developed and the interrelationships between CRs and PDCs have been determined and classified using the Kano model. The PDCs have been prioritized based on the classic HoQ and the revised HoQ. Finally the results have been compared and discussed. The television design of Entekhab Industrial Group, the largest producer of home appliance products in Iran has been selected as a case study.

Results indicated different sets of priorities derived from the two approaches. Particularly, the difference between the revised HoQ and the classic approach was due to the nonlinear relationship between CRs and PDCs.

This study was performed merely on a single product of a company, and the case study results cannot be generalized to all of the home appliance industries.

Developing the application of the Kano model in computing and revising the weights of the cells in the interrelationship matrix of HoQ is the main contribution of this paper.

This study uses a phenomenology method to investigate the experiences of married Muslim women while having romantic conversations via online dating sites during the COVID-19 pandemic. Sixteen participants were selected via purposive sampling, and the data were gathered through semi-structured interviews. The results confirm that resistance to Islamic marriage limitations is the underlying reason accounting for Muslim women's romantic chat. However, “premarital experiences in virtual space” and “chat as a remedy for loneliness” create the causal conditions of romantic chat, and “experience of family restrictions” and a “sense of freedom” provides the foundation for an online romantic chat. It is worth noting that those who voice a sense of “unhappy marriage” and “husband's sexual coldness” are more likely to turn to sex chat during the COVID-19 pandemic. The consequences of digital romantic conversations for married Muslim women are “chat addiction” and “feeling a sense of betrayal.”

The purpose of this study is to model steam condensing flows through steam turbine blades and find the most suitable condensation model to predict the condensation phenomenon.

To find the most suitable condensation model, five nucleation equations and four droplet growth equations are combined, and 20 cases are considered for modelling the wet steam flow through steam turbine blades. Finally, by the comparison between the numerical results and experiments, the most suitable case is proposed. To find out whether the proposed case is also valid for other boundary conditions and geometries, it is used to simulate wet steam flows in de Laval nozzles.

The results indicate that among all the cases, combining the Hale nucleation equation with the Gyarmathy droplet growth equation results in the smallest error in the simulation of wet steam flows through steam turbine blades. Compared with experimental data, the proposed model’s relative error for the static pressure distribution on the blade suction and pressure sides is 2.7% and 2.3%, respectively, and for the liquid droplet radius distribution it totals to 1%. This case is also reliable for simulating condensing steam flows in de Laval nozzles.

The selection of an appropriate condensation model plays a vital role in the simulation of wet steam flows. Considering that the results of numerical studies on condensation models in recent years have not been completely consistent with the experiments and that there are still uncertainties in this field, further studies aiming to improve condensation models are of particular importance. As condensation models play an important role in simulating the condensation phenomenon, this research can help other researchers to better understand the purpose and importance of choosing a suitable condensation model in improving the results. This study is a significant step to improve the existing condensation models and it can help other researchers to gain a revealing insight into choosing an appropriate condensation model for their simulations.

Failure modes and effects analysis (FMEA) is a practical tool to evaluate risks, discover failures in a proactive manner and propose corrective actions to reduce or eliminate potential risks. The purpose of this paper is to apply FMEA technique to examine the hazards associated with the process of service delivery in intensive care unit (ICU) of a tertiary hospital in Yazd, Iran.

This was a before-after study conducted between March 2013 and December 2014. By forming a FMEA team, all potential hazards associated with ICU services – their frequency and severity – were identified. Then risk priority number was calculated for each activity as an indicator representing high priority areas that need special attention and resource allocation.

Eight failure modes with highest priority scores including endotracheal tube defect, wrong placement of endotracheal tube, EVD interface, aspiration failure during suctioning, chest tube failure, tissue injury and deep vein thrombosis were selected for improvement. Findings affirmed that improvement strategies were generally satisfying and significantly decreased total failures.

Application of FMEA in ICUs proved to be effective in proactively decreasing the risk of failures and corrected the control measures up to acceptable levels in all eight areas of function.

Using a prospective risk assessment approach, such as FMEA, could be beneficial in dealing with potential failures through proposing preventive actions in a proactive manner. The method could be used as a tool for healthcare continuous quality improvement so that the method identifies both systemic and human errors, and offers practical advice to deal effectively with them.

Nowadays, the importance of infant birth weight (IBW) as a key factor in determining the future of physical and mental development of children is a growing concern. The purpose of this paper is to investigate the relationship between maternal characteristics and IBW among pregnant women who were referred to health centers in Qazvin city in the year 2016.

A descriptive-analytical study was conducted among pregnant women in 28–36 weeks of gestation who referred to healthcare centers and facilities affiliated by the Qazvin University of Medical Sciences in April–June 2016. The associations between maternal physical activity, mothers’ socioeconomic status and birth weight were examined by SPSS Software Package version 16 through linear and logistic regression tests.

Linear regression modeling suggested that maternal weight (p=0.001), income (p=0.04), gestational age of delivery (p=0.00) and pre-pregnancy BMI (p=0.02) were positively associated with birth weight, while occupational and heavy physical activity (p=0.003 and 0.008, respectively) were negatively associated with IBW. In this study, low birth weight infants are compared to those with normal weight belonged to mothers who have spent more time in doing heavy physical activities (OR=1.11, 95% CI 1.01–1.23). Also infants with low birth weight compared to others in the normal weight category were born from mothers with lower pre-pregnancy BMI (OR=0.65, 95% CI 0.62–0.78), gestational age of delivery (OR=0.82, 95% CI 0.79–0.86), maternal weight (OR=0.86, 95% CI 0.84–0.88) and income (OR=0.79, 95% CI 0.69–0.83).

The study findings revealed that certain maternal characteristics could play a significant role in IBW. Despite the importance, in most of developing countries (particularly Iran), future mothers are not advised about an appropriate weight gain during pregnancy or the optimal level of physical activity in such a period of time. Therefore, counseling pregnant women and giving them proper information on appropriate perinatal care would be helpful in order to have pregnancies with optimal outcomes.

The authors applied several statistical methods to analyze IBW among mothers with different maternal characteristics and predict birth weight based on contributing factors.

The purpose of this study is to provide a comprehensive conceptual internal whistleblowing model for accounting students and then test it empirically. To this end, by considering the mediating role of professional commitment (PC) and the moderating effect of the perceived moral intensity (PMI), the anticipatory socialization's impact on whistleblowing intentions has been investigated.

An ex-post quantitative research design has been adopted for this survey study. The statistical population of the study consisted of Iranian undergraduate accounting students close to graduation in 2020. The study utilized a self-administered survey of 211 accounting students from 8 public universities; besides, to examine the proposed hypotheses, it utilized the partial least squares structural equation modeling analysis.

The results of testing the hypotheses indicate that the constructs of anticipatory socialization (AS), PC and PMI have positive effects on whistleblowing intentions; besides, PC in the relationship between AS and whistleblowing intentions plays a complementary partial mediating role. In addition, PMI moderates the effect of PC on whistleblowing intentions, as well as the mediated effect of AS on whistleblowing intentions via PC.

The results of this study indicate the importance of considering trait syntheses of ethics education for undergraduate students, and the need to address and familiarize students with ethical principles, goals and missions of the accounting profession in society.

This study, by providing a moderated mediation conceptual model, examines the important functions that AS, PC and PMI might have created in the whistleblowing process, and also provides some empirical evidence in this area; As a result, it leads to the expansion of theoretical and empirical knowledge in this field.

This study aims to explore ways to minimize plate waste at university canteens by studying plate waste and consumers at three main canteens of a university, Thailand.

Using university canteens in Thailand as a case, plate waste was characterized and quantified and consumers’ insights concerning food consumption practices were examined through focus group discussion.

The results revealed that each consumer wasted edible food around 19 grams/meal. The generation of plate waste is affected by the food provision system, including canteen setting, food purchasing procedure and food quality. In addition, the presence of stray dogs in the canteens inhibited consumers from finishing up their food. Thus, improving the food provision system is crucial to engage consumers in achieving zero plate waste.

This research sheds some light on ways to engage consumers in sustainable consumption and contributes to the knowledge on plate waste and sustainable consumption in university settings. Improving food quality and canteen settings are of importance to better engage consumers. In addition, this research revealed that concepts of system of provision and citizen-consumers are practical to analyze sustainable transformations for green university initiatives.

Higher education institutions (HEIs) should assume their role as leaders in the search for a sustainable future. Consequently, such institutions need to incorporate sustainability into their activities. However, this needs to be done holistically and not with isolated and independent actions. Therefore, this study aims to develop a structure of sustainability action archetypes to help HEIs holistically incorporate sustainability in their strategies.

A systematic review of the literature was conducted focusing on the subject of sustainability in HEIs.

A structure of sustainability action archetypes for HEIs was proposed. Further, based on scientific literature, examples of actions were presented within each archetype.

This study provides HEI administrators and other organizations with a practical structure to enable the systemic incorporation of sustainability objectives and actions into institutional activities.

This study adapts the tool “sustainable business model archetypes” for a new purpose. This tool was initially developed to classify innovations of sustainable business models.

Educational institutions have a special social responsibility to initiate processes of sustainability transformation in society, nevertheless, activities that effectively address students as well as employees are rather moderate. To initiate change alongside strategic and political decisions, this paper aims to present findings of a gamification intervention (hereafter referred to as climate duel), which was implemented in a field study and tested to assess its potential as a supporting and transformative approach in a university context.

A three-month field study was conducted with two universities of applied sciences. The study included an environmental impact analysis of the two participating university departments, the conception and testing of the intervention and associated surveys to evaluate the effectiveness of the intervention in order to and to obtain feedback that would allow the duel to be scaled up at other universities or institutions.

Three hundred seventy-five people took part and saved 2.6 tons of greenhouse gas emissions through their participation in the climate duel and their corresponding behavioural changes. In addition, feedback from the participants yielded positive results in terms of behavioural changes and generated valuable evidence for future implementations. Nevertheless, there is still room for improvement, especially in terms of supporting communication activities that promote social relatedness to motivate each other, share experiences or deal with implementation difficulties in everyday life.

Building on the promising effects of gamification, the study is a showcase for applied science. With the possibility of testing a theory-based intervention in practice, an implementable, effective and scalable measure for universities that helps to accelerate the transformation process is available.

Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.

This paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.

It was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.

Literature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.