Smart Grid Technologies for Efficiency Improvement of Integrated Industrial Electric System

The purpose of this research is to identify the need of Smart Grid Technologies in communication between industrial plants with co-generation capability and the electric utilities in providing the most optimum scheme for buying and selling of electricity in such a way that the fuel consumption is minimized, reliability is increased, and time to restore the system is reduced. A typical industrial plant load profile based on statistical mean and variance of industrial plants\u27 load requirement is developed, and used in determining the minimum cost of producing the next megawatt-hours by a typical electric utility. The 24-hour load profile and optimal power flow program are used to simulate the IEEE 39 Bus Test System. The methodology for the use of smart grid technology in fuel saving is documented in the thesis. The results obtained from this research shall be extended to include several industrial plants served by electric utilities in future work by the UNO research team

A Machine Learning Approach to Understanding Emerging Markets

Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial Regression are promising for this problem. Additionally, data processing and cleansing is implemented on a model different than what is currently being used by the logistic provider. The results indicate that through the use of polynomial regression on pre-processed and cleaned data, a 75% improvement in performance can be achieve in comparison to the baseline established by the logistics provider

Obesity in pregnancy: risk of gestational diabetes

Background: Maternal obesity is a risk factor for gestational diabetes and other adverse pregnancy outcomes, but the body fat distribution may be a more important risk factor than body mass index. Pregnancy is an insulin resistant state and more so, in obese women. Metformin could be beneficial in obese pregnant women due to its insulin sensitizing action. The aims of this study are to investigate visceral fat mass as a risk factor for gestational diabetes (VFM study), to develop a mathematical model for the prediction of gestational diabetes in obese women (VFM study) and to examine the effect of metformin on pregnancy outcomes in obese non-diabetic women (MOP Trial). Methods and Results: VFM study: The body composition of 302 obese pregnant women was assessed using bioelectrical impedance. A mathematical model to predict gestational diabetes using machine learning was developed using visceral fat mass which is a novel risk factor in addition to conventional risk factors. 72 of the women developed gestational diabetes (GDM). These women had higher visceral fat mass. Women with a baseline visceral fat mass ≥ 75th percentile, had a 3-fold risk of subsequent gestational diabetes. The mathematical model predicted gestational diabetes with an average overall accuracy of 77.5% and predicted birth centile classes with an average accuracy of 68%. According to the decision tree developed, VFM emerged as the most important variable in determining the risk of GDM and a VFM < 210 was used as the first split in the decision tree. MOP Trial: 133 obese pregnant women were randomised to either metformin or placebo. The pregnancy outcomes were compared in both groups. Insulin resistance was measured in all women. 118 women completed the trial. Metformin did not reduce the neonatal birth weight z-score, which was the primary outcome of the trial or the incidence of large for gestational age babies. However, metformin therapy significantly reduced gestational weight gain, reduced the pregnancy rise in visceral fat mass, and attenuated the expected physiological rise in insulin resistance at 28 weeks gestation. However, this did not result in an overall significant reduction in the incidence of gestational diabetes. There was a trend towards a reduced incidence of gestational diabetes in women with high baseline insulin resistance randomised to metformin. Conclusions: Visceral fat mass is a novel risk factor for gestational diabetes. The mathematical model successfully predicted gestational diabetes. Metformin reduced gestational weight gain and insulin resistance but did not lower the median neonatal birth weight or reduce the incidence of GDM

Additive manufacturing of the high-performance thermoplastic : Experimental study and numerical simulation of the Fused Filament Fabrication

Additive manufacturing (AM) refers to a wide variety of manufacturing processes for rapid prototyping and production of final and semi-final products. In opposite to conventional orsubtractive processes, in additive manufacturing, the material is gradually added layer by layer to form the parts. AM enables the fabrication of complex parts which were impossible or not costeffective to manufacture with the traditional processes. Fused Filament Fabrication (FFF) is basedon the melting of a polymeric filament in an extruder; the filament is then deposited layer by layerto manufacture the final parts. Despite growing interest from industries and a large audience inrecent years, these manufacturing processes are still not well mastered, especially for not mass produced polymers. In this thesis, we will take an insight into the printability of PEEK(Polyetheretherketone). The aim is to find the printing conditions to obtain the best quality of theprinted parts by FFF process. In the first step, we have determined the polymer properties influencing the quality of the printed parts by FFF. The rheological properties, the surface tension,the thermal conductivity and thermal expansion have been determined experimentally. Then, thecoalescence phenomenon of the polymeric filaments has been studied by experimental, analyticaland numerical simulation. Furthermore, the stability of the filament and its flow properties when itexits from the extruder in the FFF process has been determined by experimental, analytical andnumerical simulation. Then, we have focused on the determination of the die swelling of PEEKextrudate. Lastly, the kinetics of isothermal and non-isothermal crystallization of PEEK has beenstudied by experimental study. The kinetics of crystallization has been applied to FFF process bynumerical simulation in order to determine the optimum environment temperature to control thecrystallization of printed parts. The crystallization of PEEK reaches its maximum value (about22%) of crystallization during the deposition

The sustainability of Reconstruction and Development Programme Houses : 2004 to 2006 : a case study of Litchis Bay, East London

The South African government has endeavored to provide settlement to citizens and policies have been formulated since 1994 to guide these efforts. The South African Constitution (1996, section 26) stated that everyone has the right to have access to adequate housing. Furthermore the African National Congress’s Freedom Charter (1955) made it clear that: “there shall be houses, security and comfort for all, everybody shall have the right to live where they choose, to be decently housed, and to bring up their families in comfort and security. Slums shall be demolished and new suburbs built, where all shall have transport, roads, lights, playing fields, crèches and social centers”. However, after 19 years of democracy the state of housing is still a critical issue that addresses whether development has served to further the cause of sustainable development with respect to the pillars of human settlement such as shelter, infrastructure and economic opportunities. The study was conducted in the Litchis Bay settlement in East London, Eastern Cape Province. Mixed approaches of quantitative and qualitative methods were used to solicit data. Random Sampling techniques were conducted to select the sample population. Data was captured on Excel sheets to generate graphs. Findings of the study and conclusions were drawn. Therefore when measuring the term ‘sustainable human settlement’ against the findings on RDP houses in Litchis Bay, one can conclude that sustainable human settlement is not fully absorbed in defining the sustainability of RDP houses in Litchis Bay. Moreover the majority of houses have defects, and social amenities are not easily accessible

Association between CHA2DS2-VASc score and in-hospital death in ICU patients with COVID-19

Background: CHA2DS2-VASc score is a scientifically proven risk assessment score for patients with atrial fibrillation. It may be a good predictor of in-hospital death in COVID-19 patients. The present study aimed to evaluate the association between CHA2DS2-VASc score and in-hospital mortality in the prognosis of intensive care unit (ICU) patients with COVID-19. Methods: Eighty-four COVID-19 patients who were hospitalized in the ICU were retrospectively analyzed in a tertiary health care center, and the CHA2DS2-VASc score was determined. All analyses were performed using SPSS statistical software (SPSS Inc., Chicago, IL, USA, 20.0). A p-value &lt;0.05 was considered statistically significant. Results: The median age of patients was 60.0 years, and most were males (75.0%). Findings of the study showed that the CHA2DS2-VASc score was considerably higher among the hospitalized patients than discharged patients (3.08 ± 1.72 vs. 1.38 ± 1.16; p&lt;0.001), and patients who required mechanical ventilation compared to those who did not require mechanical ventilation (3.03 ± 1.68 vs. 1.15 ± 0.97; P &lt;0.001), respectively. Patients with CHA2DS2-VASc score of ≥3 had substantially higher age [67(45-87) vs. 58(19-75); P ˂0.001], computed tomography involvement score [67.5(20-90) vs. 35(15-90); P ˂0.001] and need for mechanical ventilation [29(90.6%) vs. 22(42.3%); P ˂0.001]. A significant difference was found in oxygen saturation on admission (P =0.001) between the two groups. In-hospital death was significantly higher among patients with a CHA2DS2-VASc score of ≥3 (P &lt;0.001). The CHA2DS2-VASc score was positively correlated with white blood cells count (r=0.257, P =0.018) and negatively correlated with the number of days spent in the hospital (r=-0.184, P=0.130) due to higher in-hospital death in ICU patients with COVID-19. Conclusion: CHA2DS2-VASc score may be an effective tool to estimate in-hospital death in COVID-19 patients who were hospitalized in the ICU