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This study aims to investigate the association of vitamin D intake and the risk of depression, anxiety and stress among Tehranian female adolescents.

This cross-sectional analysis included 263 participants. A valid and reliable food frequency questionnaire was used to determine dietary intake of vitamin D. Depression, anxiety and stress scores were characterized by Depression Anxiety Stress Score-21 questionnaire. Multivariable logistic regression was used to estimate the odds ratio (OR) for the occurrence of depression, anxiety and stress according to the tertiles of vitamin D intake.

The mean ± standard deviation age and body mass index (BMI) of participants were 16.2 ± 1.0 years and 22.2 ± 4.1 kg/m², respectively. Mean score of depression, anxiety and stress of participants were 9.8 (low-grade depression), 8.4 (low-grade anxiety) and 14.0 (borderline for stress), respectively. In the final model, after adjustment for age, BMI, physical activity, mother/father’s education level, dietary fiber and total energy intake, the OR for depression in the highest compared to the lowest tertile of vitamin D intake was 0.53 (95% confidence interval [CI], 0.24–0.98) (p for trend: 0.040). Moreover, based on the fully adjusted model, participants in the highest tertile of vitamin D intake had lower odds of stress (OR: 0.49; 95% CI: 0.23–0.93), in comparison to those in the lowest one (p for trend: 0.021).

To the best of the authors’ knowledge, this is the first study to assess the association of vitamin D intake and risk of psychological disorders, including depression, stress and anxiety in Middle East and North Africa region’s female adolescents.

To the best of our knowledge, the studies on determining adherence to the Mediterranean dietary pattern (MDP) in Iran as a non-Mediterranean country are scarce. Thus, the aim of the study is to determine the adherence to the MDP in a sample of female adolescents who are residents of Tehran, Iran.

In this cross-sectional study, 263 female adolescents aged 15-18 years were studied. Information on socio-demographic, lifestyle and anthropometric variables were obtained using a structured questionnaire. Also, dietary intakes were determined using a validated 168-item food frequency questionnaire. Adherence to MDP was characterized using Mediterranean-style dietary pattern score (MSDPS).

Typically, the mean ± SD MSDPS was low in the present study (15.9 ± 5.6). The mean ± SD age and body mass index of the study population was 16.2 ± 0.9 years and 22.2 ± 4.1 kg/m², respectively. In this study, the maximum MSDPS was 34.3, which was only one-third of maximum possible score (100). Multiple linear regression analyses showed that higher MSDPS scores were positively associated with age (standardized β = 0.1; p = 0.006), higher energy intake (standardized β = 0.2; p < 0.001) and marginally higher physical activity (standardized β = 0.1; p = 0.079).

Understanding low adherence to MDP in Tehranian female adolescents can provide basic knowledge to launch systematic programmes for gravitation toward MDP.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.