Postpartum Mental Health among Young Women

Background: A number of studies have highlighted the physical health problems associated with adolescent pregnancy in Saudi Arabia , However there were few studies dealing with the postpartum psychiatric disorders .The study aims to determine the prevalence of postpartum psychological distress and to evaluate the associated risk factors in a sample of primigravid young women in Al Ahsa region, Saudi Arabia. Methods: We assessed the prevalence of postnatal mental health in 190 young mothers attending the maternity hospital using general health questionnaire. We also assessed the relationship between socio-demographic, psychiatric and obstetric risk factors and the mental health. Results: The percent of women with psychological distress was 35.2%. Significant risk of psychological distress was associated with several socio-demographic, psychiatric and obstetric risk factors. Only four items were found to be significant predictors of postpartum psychological distress; low family income, poor husband support, birth of female baby and gestational diabetes. Conclusions: These results highlighted importance of screening for psychological distress and its associated risk factors in the implementation of proper perinatal care for the pregnant Saudi adolescents

Modelling of Multiphase Fluid flow in Heterogeneous Reservoirs

Computational modeling of multiphase fluid flow in highly heterogenous problems with complicated geometries is a challenging problem for reservoir engineers, with a rich research in establishing best methods and approaches. The novelty in this work is centered around the implementation and comparison of simulation results from two software - the open source ICFESRT and the commercial software ECLIPSE - for a two-phase multiphase problem (oilwater) in both simple and complex geometries. The work involves: (a) implementation and comparison of simulation results from the two software on three different, hypothetical but typical geometries; (b) consideration of a real field case and the associated data analysis, rock characterization, and geostatistics of a real field representative of a highly heterogeneous reservoir; and (c) implementation of both software on the real field case for predictions of oil production at the site, and comparison of the simulation results from the two software. The initial comparison of simulation results for was carried out using three hypothetical (but common) geometries, these being: (a) a quarter five spot with one geological layer; (b) the same geometry as in (a) but with a vertical heterogeneity i.e. 5 different geological layers; (c) and lastly a full 5 spot with 5 different geological layers was implemented. Three different mesh resolutions were applied in both software and comparisons were carried out for mesh-independency. The results showed that in all these three scenarios, good agreement was observed between IC-FERST (coarse mesh) and ECLIPSE (fine mesh) with an average percentage difference at the production well ranging between 2.5% and 10.5% for the oil production and 12% and 26% for the water production. Both the ICFERST and ECLIPSE were subsequently implemented on a real, heterogeneous field – which consisted of 25 producing wells and 8 injections wells. Prior to the software implementation, a data analysis and rock characterization was carried out –Using data from the 33 wells. The logging and core data (a total of 30,000 log readings and 1150 core samples) were utilized and a novel rock characterization technique -Balaha Rock Characterization Code- was implemented to allow for the optimal clustering of rock types within the reservoir, The rock characterization resulted in identifying 7 rock types with their unique porosity-hydraulic permeability relationships. Subsequently, geostatistical methods were implemented – which enabled populating the computational cells of the two software with the corresponding reservoir properties (porosity, hydraulic permeability). To achieve the property population into the unstructured computational domain of the ICFERST software, a newly-developed script was written in Matlab and Python. The rock properties data populated on IC-FERST consist of porosity, permeability, relative permeability, capillary pressure and connate water saturation. A further comparison between the IC-FERST simulation results with the corresponding ECLIPSE simulations was carried out – were all simulations were carried out for a period of 40 years. The percentage differences between the two software simulations were estimated for : (i) ten individual production wells and (ii) the total of all production wells. The results showed that a good agreement exists between the IC-FERST and ECLIPSE simulations, with an average percentage difference for the total oil production of 10.5%, the total water production of 26% and the total water injection of 14%. The results for the ten individual wells showed an average percentage difference of 15.5% ranging from 3 to 29% for the oil production in the late time period. Slightly higher differences were observed when the overall period was considered, due to the large difference at the early time period of the simulation. The results indicated that IC-FERST, when incorporating the necessary rock characterization information – which highlight the heterogeneity of the reservoir – can produce results that can compete with the industry standard ECLIPSE. Additional aspects need to be considered within the current real field IC-FERST simulation, the inclusion of possible fractures and faults, as these were incorporated in the computational domain of ECLIPSE. Additional capabilities also still need to be embedded into IC-FERST, such as the incorporation of the fluid density and viscosity variations with pressure and the consideration of the volume factors, in order to enhance its competitiveness with existing commercial reservoirs simulators such as ECLIPSE

The phenomenology of premenstrual syndrome in female medical students: a cross sectional study

Background: The premenstrual syndrome (PMS) is particularly common in the younger age groups and, therefore represents a significant public health problem in young girls. This study aims to estimate the prevalence, severity, determinants of premenstrual syndrome (PMS) and its impact among the female medical students in Al-Ahsa, Saudi Arabia. Method: This study was performed at the College of Medicine, King Faisal University, Saudi Arabia, from June through December 2009. It included 250 medical students. They filled different questionnaires covering American College of Obstetrics and Gynecology (ACOG) criteria to diagnose PMS, demographic & reproductive factors, physical activity and mental condition. Regression analysis was conducted for all the predictors. Results: PMS was diagnosed in 35.6% of cases, distributed as 45% mild, 32.6% moderate and 22.4% severe. There were significant trends for older age, rural residence, family income and family history of PMS. The dominant limited activity was concentration in class (48.3%). Limitations of activities were significantly more frequent among severe cases. The prevalence of anxiety and depression was statistically more evident in the PMS group. Regression analysis revealed that, PMS was significantly associated with older age groups, rural residence, lower age at menarche, regularity of menses and family history. Conclusion: PMS is a common problem in young Saudi students in Al Ahsa. Severe PMS was associated with more impairment of daily activities and psychological distress symptoms. Older student age, rural residence, earlier age of menarche, regular cycles and positive family history are possible risk factors for PMS

Effect of using ground waste tire rubber as fine aggregate on the behaviour of concrete mixes

427-435The development of environmentally accepted methods of used tire disposal is one of the greatest challenges that waste management experts face today. Using of wastes and by-products as concrete aggregate has attained great potential in the last few years. The aim of this work is to investigate the possibility of the usage of ground waste tire rubber (GWTR) in the civil construction as a partial replacement for fine aggregates and the influence of these wastes on the properties of ordinary concrete. The cement content for concrete mixes is 300, 400, and 500 kg/m³. The total fine aggregate (TFA) in all mixes is sand, which is partially replaced by GWTR particles. The percentages by volume of GWTR/TFA are 5%, 10%, 15% and 20%. The physical and mechanical properties of rubberized concrete are compared with those of ordinary concrete mixes. Also, three treated materials, polyvinyl acetate, silica fume and sodium hydroxide (PVA, SF and NaOH) are used for treatment the ground waste tire rubber to improve the interface friction between rubber particles and cement matrix. The results show that the mass density (bulk density) of hardened rubberized concrete decreases with increasing rubber content, this is an advantage for that concrete application. Also concrete specimens containing rubber particles are much tougher than those without rubber particles. The damping ratio of the rubberized concrete containing 20% rubber is much higher than those of normal concrete by about 63.2%. Rubberized concrete incorporating treated rubber particles gives better results than concrete incorporating normal rubber

Influence of aggregate type on mortar thermal stability

217-224<span style="font-size: 13.0pt;mso-bidi-font-size:8.0pt;font-family:" times="" new="" roman","serif""="">The contributions of components, especially aggregates, to the fire resistance of mortar and concrete have been studied. Blast-furnace slag, as a by-product from iron and steel industries, is allowed to cool slowly in air to form dense slag or to cool very rapidly in water to form granulated slag. The present work aims to study the effect of using crushed air-cooled and water-quenched slags as a line aggregate for replacement of sand on the mechanical and chemical properties of mortars exposed to lire at different temperatures. Mortar mixes were prepared with three different water cement ratios (0.4, 0.5, and <span style="font-size: 13.0pt;mso-bidi-font-size:8.0pt;font-family:" times="" new="" roman","serif""="">0.6) and cured in potable water for 90 days, then kept in laboratory atmosphere conditions for about four months. They were exposed to lire at temperatures of 300,400, 500, and 600°C for 2 h soaking time followed by rapid cooling in water. Results <span style="font-size: 13.0pt;mso-bidi-font-size:8.0pt;font-family:" times="" new="" roman","serif""="">of this investigation indicated that the compressive strength of mortars exposed to lire was gradually decreasing with increasing temperature of fire up to 500°C, while it significantly decreased when temperature reached 600°C. The use of air cooled <span style="font-size:13.0pt;mso-bidi-font-size:8.0pt;line-height:115%; font-family:" times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";="" mso-fareast-theme-font:minor-fareast;mso-ansi-language:en-us;mso-fareast-language:="" en-us;mso-bidi-language:ar-sa"="">slag improved the thermal stability of mortar.</span

Human Action Recognition Based on Transfer Learning Approach

Human action recognition techniques have gained significant attention among next-generation technologies due to their specific features and high capability to inspect video sequences to understand human actions. As a result, many fields have benefited from human action recognition techniques. Deep learning techniques played a primary role in many approaches to human action recognition. The new era of learning is spreading by transfer learning. Accordingly, this study&#x2019;s main objective is to propose a framework with three main phases for human action recognition. The phases are pre-training, preprocessing, and recognition. This framework presents a set of novel techniques that are three-fold as follows, (i) in the pre-training phase, a standard convolutional neural network is trained on a generic dataset to adjust weights; (ii) to perform the recognition process, this pre-trained model is then applied to the target dataset; and (iii) the recognition phase exploits convolutional neural network and long short-term memory to apply five different architectures. Three architectures are stand-alone and single-stream, while the other two are combinations between the first three in two-stream style. Experimental results show that the first three architectures recorded accuracies of 83.24&#x0025;, 90.72&#x0025;, and 90.85&#x0025;, respectively. The last two architectures achieved accuracies of 93.48&#x0025; and 94.87&#x0025;, respectively. Moreover, The recorded results outperform other state-of-the-art models in the same field

Predictors of Fetal Demise After Trauma in Pregnant Saudi Arabian Women

In Saudi Arabia, road traffic crashes are becoming a serious public health problem and there are no recent, large-scale, published reports discussing maternal and fetal injuries. We aimed to explore the predictors of fetal death/abortion after maternal trauma. A retrospective case-control study was performed exploring cases of maternal trauma. The study group included 118 women with pregnancy loss while 308 women without loss represented the control group. All data were compared using univariate analysis followed by multivariate regression analysis. Only 3 predictors were associated with significant effect on pregnancy loss after trauma (P \u3c 0.05): second trimester of pregnancy (OR 2.77, 95% CI: 1.66-4.63, placental abruption (OR 3.69, 95% CI: 2.01-6.79) and severe injury score (OR 6.78, 95% CI: 4.04-11.37)