Search results for: Hammoud Al Joumaa

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

Abstract:

When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Hasan Hassoun, Jaafar Hallal, Denis Duhamel, Mohammad Hammoud, Ali Hage Diab

Abstract:

This paper aims to prove the accuracy of an inexpensive smartphone camera as a non-contact vibration sensor to recover the vibration modes of a vibrating structure such as a cantilever beam. A video of a vibrating beam is filmed using a smartphone camera and then processed by the motion magnification technique. Based on this method, the first two natural frequencies and their associated mode shapes are estimated experimentally and compared to the analytical ones. Results show a relative error of less than 4% between the experimental and analytical approaches for the first two natural frequencies of the beam. Also, for the first two-mode shapes, a Modal Assurance Criterion (MAC) value of above 0.9 between the two approaches is obtained. This slight error between the different techniques ensures the viability of a cheap smartphone camera as a non-contact vibration sensor, particularly for structures vibrating at relatively low natural frequencies.

Keywords: modal analysis, motion magnification, smartphone camera, structural vibration, vibration modes

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Authors: Majeda Hammoud

Abstract:

Background: Anemia during pregnancy increases the risk of delivery by cesarean section, low birth weight, preterm birth, perinatal mortality, stillbirth, and maternal mortality. In this study, we aimed to assess the prevalence of anemia in pregnant women and its associated factors in the Kuwait birth cohort study. Methods: The Kuwait birth cohort (N=1108) was a prospective cohort study in which pregnant women were recruited in the third trimester. Data were collected through personal interviews with mothers who attend antenatal care visits, including data on socio-economic status and lifestyle factors. Blood samples were taken after the recruitment to measure multiple laboratory indicators. Clinical data were extracted from the medical records by a clinician including data on comorbidities. Anemia was defined as having Hemoglobin (Hb) <110 g/L with further classification as mild (100-109 g/L), moderate (70-99 g/L), or severe (<70 g/L). Predictors of anemia were classified as underlying or direct factors, and logistic regression was used to investigate their association with anemia. Results: The mean Hb level in the study group was 115.21 g/L (95%CI: 114.56- 115.87 g/L), with significant differences between age groups (p=0.034). The prevalence of anemia was 28.16% (95%CI: 25.53-30.91%), with no significant difference by age group (p=0.164). Of all 1108 pregnant women, 8.75% had moderate anemia, and 19.40% had mild anemia, but no pregnant women had severe anemia. In multivariable analysis, getting pregnant while using contraception, adjusted odds ratio (AOR) 1.73(95%CI:1.01-2.96); p=0.046 and current use of supplements, AOR 0.50 (95%CI: 0.26-0.95); p=0.035 were significantly associated with anemia (underlying factors). From the direct factors group, only iron and ferritin levels were significantly associated with anemia (P<0.001). Conclusion: Although the severe form of anemia is low among pregnant women in Kuwait, mild and moderate anemia remains a significant health problem despite free access to antenatal care.

Keywords: anemia, pregnancy, hemoglobin, ferritin

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Authors: Majeda S. Hammoud

Abstract:

Background and Aim: Gestational diabetes mellitus (GDM) is a common pregnancy complication affecting around 14% of pregnancies globally that carries short and long-term consequences to the mother and her child. Pre-pregnancy overweight or obesity is the most consistently and strongly associated modifiable risk factor with GDM development. This analysis aimed to determine whether vitamin D status during pregnancy modulates the effect of pre-pregnancy obesity/overweight on GDM risk while stratifying by maternal age. Methods: Data from the Kuwait Birth Cohort (KBC) study were analyzed, which enrolled pregnant women in the second or third trimester of gestation. Pre-pregnancy body mass index (BMI; kg/m2) was categorized as under/normal weight (<25.0), overweight (25.0 to <30.0), and obesity (≥30.0). 25 hydroxyvitamin D levels were measured in blood samples that were collected at recruitment and categorized as deficiency (<50 nmol/L) and insufficiency/sufficiency (≥50 nmol/L). GDM status was ascertained according to international guidelines. Logistic regression was used to evaluate associations, and adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated. Results: The analyzed study sample included a total of 982 pregnant women, with a mean (SD) age of 31.4 (5.2) years. The prevalence of GDM was estimated to be 17.3% (95% CI: 14.9-19.7), and the prevalence of pre-pregnancy overweight and obesity was 37.8% (95% CI: 34.8-40.8) and 28.8% (95% CI: 26.0-31.7), respectively. The prevalence of gestational vitamin D deficiency was estimated to be 55.3% (95% CI: 52.2-58.4). The association between pre-pregnancy overweight or obesity with GDM risk differed according to maternal age and gestational vitamin D status (Pinteraction[BMI × age × vitamin D = 0.047). Among pregnant women aged <35 years, prepregnancy obesity compared to under/normal weight was associated with increased GDM risk among women with gestational vitamin D deficiency (aOR: 3.65, 95% CI: 1.50-8.86, p = 0.004) and vitamin D insufficiency/sufficiency (aOR: 2.55, 95% CI: 1.16-5.61, p = 0.019). In contrast, among pregnant women aged ≥35 years, pre-pregnancy obesity compared to under/normal weight was associated with increased GDM risk among women with gestational vitamin D deficiency (aOR: 9.70, 95% CI: 2.01-46.69, p = 0.005), but not among women with vitamin D insufficiency/sufficiency (aOR: 1.46, 95% CI: 0.42-5.16, p = 0.553). Conclusion: The effect of pre-pregnancy obesity on GDM risk is modulated by maternal age and gestational vitamin D status, with the effect of pre-pregnancy obesity being more pronounced among older pregnant women (aged ≥35 years) with gestational vitamin D deficiency compared to those with vitamin D insufficiency/sufficiency. Whereas, among younger women (aged <35 years), the effect of pre-pregnancy obesity on GDM risk was not modulated by gestational vitamin D status. Therefore, vitamin D supplementation among pregnant women, specifically older women with pre-pregnancy obesity, may mitigate the effect of pre-pregnancy obesity on GDM risk.

Keywords: gestational diabetes mellitus, vitamin D, obesity, body mass index

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