Search results

In this paper the authors review the recent numerical techniques proposed to solve the forward and inverse problems concerning the electromagnetic casting and electromagnetic levitation techniques of the metallurgical industry. In addition, the authors present a new topology optimization method to solve the inverse axisymmetric electromagnetic levitation problem.

The proposed method is based on an exact second-order topological expansion of a Kohn–Vogelius-like functional specially devised for this problem.

Through some examples the authors show that it can find suitable solutions efficiently.

The new method completes the set of efficient methods available to solve the inverse electromagnetic casting and the inverse axisymmetric electromagnetic levitation problems.

Studies investigating the association between dietary inflammatory index (DII) and body mass index (BMI) have led to inconsistent findings. Therefore, to decisively conclude, this paper aims to clarify the relationship between DII and obesity by performing meta-analysis.

PubMed, Scopus and Google Scholar were searched up to July 2017 using key words selected from Medical Subject Headings and other related keywords to identify all relevant articles. In total, 22 articles were entered into the meta-analysis; 22 studies compared the mean of BMI among subjects with highest versus the lowest DII and 4 studies had data on the hazard risk (HR) or odds ratio (OR) for obesity.

A meta-analysis on included studies indicated a significant association on either mean differences (MD) in BMI (MD = 0.811; 95 per cent CI: 0.365-1.256; p: 0.0001) or obesity OR (OR: 1.310; 95 per cent CI: 1.144-1.500; p = 0.000) by comparing the highest and lowest DII categories. Between-study heterogeneity was high (Cochrane Q test, p < 0.001, I² = 98.1 per cent, df = 21, τ² = 0.9273), and only dietary assessment methods could explain the source of heterogeneity in which 24-h dietary recalls were homogeny (I² = 8.4 per cent, df = 2, p = 0.335).

The results of the present meta-analysis suggest that adherence to high DII score increased BMI and obesity. More prospective studies in different populations are needed to better clarify this relation.

This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.

The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.

The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.

Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.

The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.

This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.

This paper aims to evaluate macro- and micronutrient intake among Jordanian adults who have metabolic syndrome (MetS) and those MetS-free.

A total of 154 Jordanian adults aged 20-55 years were enrolled from The National Center for Diabetes, Endocrinology and Genetics in this case control study. In total, 77 newly diagnosed cases of MetS and 77 controls were recruited and matched by age and sex. Dietary data were collected by face-to-face interview using a validated food frequency questionnaire.

After adjustment of the energy intake and macronutrients, the cases reported significantly higher intake of the amount of protein (p < 0.001), carbohydrates (p < 0.001), fiber (p < 0.001), sugar (p < 0.001), fat (p < 0.001), saturated fat (p < 0.001), monounsaturated (p < 0.001), polyunsaturated (p < 0.001), trans-fat (p < 0.029), omega-3 (p < 0.001) and omega-6 (p < 0.001) as compared to the controls. The results also showed that the intake of some micronutrients was significantly higher among cases when compared to the controls. In this study, three dietary patterns have been itemized; “fast food dietary pattern”, “Mediterranean dietary pattern” and “high-protein dietary pattern”. Only a direct significant trend between MetS and fast food pattern was detected (p-trend = 0.001). Neither significant associations nor trends were detected in the Mediterranean and high-protein patterns with MetS risk.

The study findings highlighted the presence of associations between fast food dietary pattern, total energy and some macro- and micronutrients intake and the risk of MetS among population living in the Middle East. Even though the findings of this study bridge the gap between theory and practice, a large scale population-based study is warranted to confirm these findings.