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The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.

The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.

Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.

To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

The purpose of this paper is to explore the influence of organizational culture and leadership style on employees’ job satisfaction, organizational commitment and work motivation in the educational sector in the state of Qatar.

The study was conducted using a questionnaire with a sample size of 364 employees in the educational sector in Qatar. Data were analyzed using factor analysis, Pearson correlation and multiple linear regression, were employed to examine the relationships between the variables under investigation.

Significant positive relationships were observed between supportive culture and job satisfaction; supportive culture and organizational commitment; participative-supportive leadership and job satisfaction; directive leadership and job satisfaction; job satisfaction and work motivation; job satisfaction and organizational commitment.

This paper would help managers and policy-makers in the education sector to develop a better understanding of organizational culture and leadership styles and their influence on employee satisfaction, commitment and motivation.

The education sector is experiencing a fast growth in Qatar due to significant outlays by the government. This study is among the first in the country to understand the variables affecting employees’ performance in education sector.

Deficiencies or imbalances in dietary fat intake may influence on mental and neurological functions of children with autism spectrum disorders (ASD). This study aims to compare body mass index (BMI) and the amount of fatty acids intake in the autistic patients with the comparison group.

This case-control was carried out on 200 randomly selected children from 5 to 15 years old (100 autistic patients as the case group and 100 healthy children as the comparison group) in Tehran, Iran. The food frequency questionnaire (FFQ) was used to assess the intake of calorie, macronutrients and different types of dietary fatty acids including saturated fatty acids (SFA), monounsaturated fatty acids (MUFAs), poly unsaturated fatty acids (PUFAs), linoleic acid (LA), α-Linolenic acid (ALA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and trans fatty acids.

The autistic patients had higher BMI, birth weight and mother’s BMI compared to the comparison group (All p < 0.01). No significant difference was found in the amount of dietary calorie, protein, carbohydrate and total fat intake between two groups. The risk of ASD was associated with higher intake of MUFAs (OR: 3.18, CI%:1.13–4.56, p = 0.04), PUFAs (OR: 4.12, CI95%: 2.01–6.25, p < 0.01) and LA (OR: 4.76, CI95%: 1.34–14.32, p < 0.01).

The autistic children had higher BMI and higher intake of unsaturated fatty acids except for omega-3 fatty acids. Further longitudinal studies are warranted.

The COVID-19 pandemic has caused significant mortality and morbidity worldwide. However, the role of dietary patterns as a potential risk factor for COVID-19 has not been well established, especially in studies with large samples. Therefore, this study aims to identify and evaluate the association between major dietary patterns and COVID-19 among adults from Iran.

In this cross-sectional study, the authors included 9,189 participants aged 20–70 who participated in the Yazd Health Study (YaHS) and Taghzieh Mardom-e-Yazd study (TAMIZ). They used factor analysis to extract dietary patterns based on a food frequency questionnaire (FFQ). Then, they assessed the relationship between these dietary patterns and the odds of COVID-19.

This study identified two major dietary patterns: “high protein and high fiber” and “transitional”. Participants in the highest tertile of the “high protein and high fiber” dietary pattern, which included vegetables, fruits, dairy and various kinds of meats such as red meat, fish and poultry, had a lower odds of COVID-19 compared with those in the lowest tertile. However, the “transitional” dietary pattern did not affect the risk of COVID-19.

In conclusion, a “high protein, high fiber” diet may lower the odds of COVID-19. This study suggests that dietary patterns may influence the severity and spread of future similar pandemics.

The purpose of this study is to explore the association between intake of different types of dietary fats with breast cancer (BC) risk in Iranian women.

A total of 540 women (180 women with BC and 360 healthy women) were recruited from Shohadaye Tajrish hospital, Tehran, Iran. Data on anthropometric measurements, physical activity, smoking and alcohol consumption were collected. The food frequency questionnaire was used to assess the intake of fatty acids including saturated fatty acids, mono unsaturated fatty acids, poly unsaturated fatty acids, macronutrients, total fat, cholesterol, and calorie.

The cases had significantly higher BMI (29.19 ± 3.2 vs 27.27 kg/m2 ± 2.8) and higher intake of calorie (2737 ± 925 vs 2315 ± 1066 kcal/d, P = 0.01), carbohydrate (402 ± 125 vs 312 ± 170 kcal/d, P = 0.01) and ω−6 fatty acids (5.45 ± 6.9 vs 3.39 ± 0.59 g/d, P = 0.001) compared to the control group . Higher consumption of ω−6 fatty acids was related with higher risk of BC (OR = 5.429, CI95%:2.5–11.79, P = 0.001) The association between BC and intake of omega-6 fatty acids remained significant after adjustments for age, BMI, for using alcohol drinks, smoking, physical activity, calorie intake, protein intake and carbohydrate intake.

There are insufficient studies to investigate the association of different types of fatty acids with BC. This study found that higher omega-6 fatty acids intake was associated with increased risk of BC in women.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.