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The purpose of this study is to evaluate the relationship between weight status and food environments, peer influence and dietary intake among high-school students in Shiraz, Iran.

Applying a case-control design, 406 adolescents (n = 203 overweight or obese and n = 203 normal weight) aged 14–18 years were selected using a multistage cluster random sampling method. Demographic information, physical activity level and anthropometric indices were collected. Dietary intake was determined using a 147-item food frequency questionnaire. Food environment (home and out of home) and peer influence were determined by a validated questionnaire.

The type of food purchased using pocket money was different between adolescents with overweight or obesity and normal weight adolescents (p < 0.001). The out-of-home food environment score was not different between groups, but the home food environment score (p = 0.004) and the peer influence score (p = 0.001) were higher in normal weight adolescents. Adolescents with overweight or obesity consumed higher amounts of carbohydrate (p = 0.006) and lower amounts of protein (p = 0.01) and more sweet junk foods (p = 0.01), nonstarchy vegetables (p = 0.03) and fruits (p = 0.01) compared to the normal weight group.

Home food environment, peer influence, differences in macronutrient intake and dietary patterns may be contributing factors to adolescent weight status.

The high demand for fast, energy-efficient, compact computational blocks in digital electronics has led the researchers to use approximate computing in applications where inaccuracy of outputs is tolerable. The purpose of this paper is to present two ultra-high-speed current-mode approximate full adders (FA) by using carbon nanotube field-effect transistors.

Instead of using threshold detectors, which are common elements in current-mode logic, diodes are used to stabilize voltage. Zener diodes and ultra-low-power diodes are used within the first and second proposed designs, respectively. This innovation eliminates threshold detectors from critical path and makes it shorter. Then, the new adders are employed in the image processing application of Laplace filter, which detects edges in an image.

Simulation results demonstrate very high-speed operation for the first and second proposed designs, which are, respectively, 44.7 per cent and 21.6 per cent faster than the next high-speed adder cell. In addition, they make a reasonable compromise between power-delay product (PDP) and other important evaluating factors in the context of approximate computing. They have very few transistors and very low total error distance. In addition, they do not propagate error to higher bit positions by generating output carry correctly. According to the investigations, up to four inexact FA can be used in the Laplace filter computations without a significant image quality loss. The employment of the first and second proposed designs results in 42.4 per cent and 32.2 per cent PDP reduction compared to when no approximate FA are used in an 8-bit ripple adder.

Two new current-mode inexact FA are presented. They use diodes as voltage regulators to design current-mode approximate full-adders with very short critical path for the first time.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

Plant-based diets have been related to decreasing morbidity and mortality of many non-communicable diseases. The purpose of this study was to investigate the relationship between plant-based diets and lipid profiles and anthropometric indices.

This cross-sectional study was performed on 236 men and women selected from Shiraz health-care centers. This study used a 168-item food frequency questionnaire to calculate plant-based diet index (PDI), healthy plant-based diet index (hPDI) and unhealthy plant-based diet index (uPDI). Total cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol and triglycerides were measured. Furthermore, body mass index, a body shape index and conicity index (CI) were calculated after measuring weight, height and waist circumference.

Higher score of PDI was significantly related to higher triglycerides level (OR = 2.16; 95% CI: 1.04, 4.48; P = 0.03). In the fully adjusted model, there was a significant association between ABSI and hPDI (OR = 4.88; 95% CI: 1.17, 20.24; P = 0.03). A significant inverse association was observed between uPDI and high-density lipoprotein (HDL) (OR = 0.45; 95% CI = 0.21, 0.98; P = 0.03). Also, this study found a decreasing, but insignificant trend in relation of ABSI (OR = 0.72; 95% CI = 0.22, 2.34) and CI (OR = 0.41; 95% CI = 0.06, 0.56) with PDI.

Further studies are needed to explore the association of PDI with anthropometric indices and lipid profile and also to assess the potential causality of the observed associations. Plant-based diets according to their contents could affect triglycerides, HDL and anthropometric properties.

Hence, dietitians should consider the findings of this study such as the inverse effect of unhealthy plant-based diets on HDL and the relation between healthy plant-based diets and WC and abdominal obesity.

This study showed that adherence to a plant-based diet was related to higher triglycerides levels. Also, uPDI was inversely associated with HDL level. Furthermore, participants who adhered more to a healthy plant-based diet had higher abdominal adiposity.

The purpose of this paper is to develop a new mathematical method for the reliability analysis and evaluation of multi-state system (MSS) reliability that agrees with specifics of such system. It is possible based on the application of multiple-valued logic (MVL) that is a natural extension of Boolean algebra used in reliability analysis.

Similar to Boolean algebra, MVL is used for the constriction of the structure function of the investigated system. The interpretation of the structure function of the MSS in terms of MVL allows using mathematical methods and approaches of this logic for the analysis of the structure function.

The logical differential calculus is one of mathematical approaches in MVL. The authors develop new method for MSS reliability analysis based on logical differential calculus, in particular direct partial logical derivatives, for the investigation of critical system states (CSSs). The proposed method allows providing the qualitative and quantitative analyses of MSS: the CSS can be defined for all possible changes of any system component or group of components, and probabilities of this state can also be calculated.

The proposed method permits representing the MSS in the form of a structure function that is interpreted as MVL function and provides the system analyses without special transformation into Boolean interpretation and with acceptable computational complexity.

This paper employs new methods to evaluate the availability of multistate series–parallel systems, in which a number of similar components are available in each subsystem.

In this paper, polynomial distribution function (PDF) is combined with universal generating function (UGF) and recursive algorithm (RA) methods to evaluate the availability of multistate series–parallel systems. To achieve this goal, the PDF is initially used to determine the performance rates and the probabilities corresponding to the performance states of the similar components in a subsystem. The obtained results are used to evaluate the system availability via the UGF and RA methods.

It is shown that the combined UGF and PDF (UGF-PDF) and also the combined RA and PDF (RA-PDF) methods require less computational time than did the UGF and RA methods, respectively.

In the UGF and RA methods, there is no difference in system availability evaluation time when considering similar or different components in each subsystem. But the proposed methods in this article do not have this restrictions; therefore, these methods can be used to evaluate system availability in optimal redundancy allocation problems. As a result, using these methods reduces the optimization time of those problems.

The main aim of this paper is to develop a supply chain efficiency framework to improve overall business performance in the competitive era. This paper offers a critical literature review on supply chain efficiency that aims to reveal the basic research that has been carried out, the problem areas and requirements for the efficiency in the new era of the supply chain.

The methodology followed during this research involves beginning with a wide base of articles lying at the supply chain intersection, performance measurement topics, and then screening the list to concentrate on supply chain efficiency.

Findings show that supply chain efficiency in the modern era remains an open research field. This research contributes to the supply chain literature by clarifying the supply chain efficiency definition, defining key measurements and variables for supply chain efficiency and developing a supply chain efficiency framework to improve overall performance.

This study will be very useful to the scholars working in this field. The proposed framework would help researchers and academicians to understand every dimension and variable of supply chain efficiency, allowing practitioners to measure efficiency levels and identify improvement measures. This framework would also act as a comprehensive guide for future studies and business practices.

As there are several state-of-the-art review papers on various supply chain areas, there is a lack of literature available on supply chain efficiency studies that can provide a comprehensive framework for researchers on related literature. Thus, the present study seeks to bridge this gap in the supply chain literature. Also, this study will provide a strong basis for researchers and academicians to apply the supply chain efficiency measurement system to the dynamic supply chain.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

This study aims to (1) develop an artificial intelligence (AI)-based model to accurately forecast rebar prices and (2) propose procurement strategies to reduce the subjectivity involved in rebar price trend forecasting and minimize procurement costs for construction project general contractors.

Correlation analysis was used to identify the key factors influencing changes in rebar prices over time. An AI-based inference model, symbiotic bidirectional gated recurrent unit (SBiGRU), was developed for rebar price forecasting. The performance of SBiGRU was compared with other AI techniques, and procurement strategies based on the SBiGRU model were proposed.

The SBiGRU model outperformed the other AI techniques in terms of rebar price forecasting accuracy. The proposed rebar price forecasting model (RPFM) and procurement patterns, which integrate inventory management principles and rebar price forecasts, were demonstrated to effectively optimize procurement costs, realizing a remarkable 6.13% reduction in procurement expenses compared to the conventional monthly procurement approach.

The accuracy of AI models may be impacted by disparities in the data used for model training. Future research should explore approaches incorporating price predictions and order factors.

This study significantly extends the bounds of traditional rebar price prediction by integrating AI-driven forecasting with inventory management principles, highlighting the potential of AI-based models to improve construction industry procurement practices, reduce related risks and costs, optimize project operations and maximize project outcomes.

Developing strategic relationships with third-party logistics (3PL) providers has long been one of the key challenges in automotive supply chains. The purpose of this paper is to propose a new approach for evaluation and indexing 3PL providers using the Kano model.

The statistical population used in this research comprises managers from the Iranian automotive industry. The Kano evaluation approach is used to analyze the data collected and to classify the criteria used in selection of the preferred 3PL providers.

The results suggest that the proposed framework, based on the Kano classification, can be a powerful tool for the automotive industry in evaluating 3PL providers. Moreover, the analyses indicate that 3PL providers need to improve their service offering in aspects that are to be found in the Kano model’s requirements, namely, must-be, one-dimensional and attractive.

This study contributes to the supply chain management literature by being the first to classify selection indices of 3PL providers in the automotive industry using the Kano model.