Utilization of Batteries in The Momentary Load Variations of a Cruise Ship

The shipping and cruising industry is considered one of the most important and cheapest transportation, however, it is considered responsible for almost 2.89% of global emissions in 2018. Due to the new regulations provided by IMO, the need to reduce fuel consumption and emissions from the shipping industry becomes imperative. Several technologies have been applied to achieve those challenges, but the main focus of this thesis will be on the utilization of batteries as one of the most promising energy storage technologies, to handle the load variation rather than the operation of the auxiliary diesel engines at an economical loading range. In cruise ship applications, the auxiliary diesel engines are utilized to supply the power required for the auxiliary loads and thruster motors, usually, thruster motors operate close to harbors. So, to ensure power availability, the auxiliary diesel engines usually run at low loading levels. The optimum operating point for the diesel engines is at 80% of loading, if that percentage decreases, both fuel consumption, and NOx emissions increased exponentially, moreover, the engine’s lifetime will be reduced and more maintenance will be required. By utilizing batteries, it will be capable of providing the required power for the operation of thruster motors or during peak loading periods rather than the operation of all available auxiliary diesel engines at low loading levels. The presented study focused on four different scenarios with different battery-pack sizes, showing the space required for each scenario and the operating profile of each diesel engine indicating the fuel consumption with and without the presence of batteries. The first scenario utilized a 940-kWh battery pack, which increased the efficiency of the running engines close to the optimum operating level. The last scenario utilized a 3240-kWh battery pack, which enables the shutdown of the auxiliary engines during the operation of thruster motors or peak loading. By using the large battery model scenario, half the number of diesel engines will not be required in the future new builds of a cruise ship. This will not only improve the fuel consumption efficiency and reduce emissions, moreover, the maintenance and overall build cost will also be reduced. Technical and economic analysis is presented showing the payback period of the batteries with different fuel and battery price options. The payback period is highly affected by the saving associated with fuel costs and the price of batteries

A Proposed Modeling Framework on Innovative Work Behavior among Employees in the Malaysian Islamic Banking Industry

Responsible innovation has gained public interest in the Islamic banking industry in Malaysia. The study makes an effort to propose a modeling framework on factors affecting innovative work behavior among employees in the Malaysian Islamic banking industry. The proposed modelling framework will be based on previous literatures and the underpinning theories related to innovative work behavior. Based on the existing gaps, we propose two independent variables (i.e. Transformational leadership & personality) and a mediating variable (job satisfaction). In this study, five hypotheses are developed to determine the relationship between innovative work behavior and the determinant factors. Based on this framework, a pilot study will be conducted to test the validity and reliability of the model proposed. We expect the results to be consistent with previous research. Keywords: Islamic Banking, Innovative Work Behavior, Transformational Leadership, Personality, Job Satisfactio

Commercial wind turbines modeling using single and composite cumulative probability density functions

As wind turbines more widely used with newer manufactured types and larger electrical power scales, a brief mathematical modelling for these wind turbines operating power curves is needed for optimal site matching selections. In this paper, 24 commercial wind turbines with different ratings and different manufactures are modelled using single cumulative probability density functions modelling equations. A new mean of a composite cumulative probability density function is used for better modelling accuracy. Invasive weed optimization algorithm is used to estimate different models designing parameters. The best cumulative density function model for each wind turbine is reached through comparing the RMSE of each model. Results showed that Weibull-Gamma composite is the best modelling technique for 37.5% of the reached results

Non-Palpable Invasive Lobular Carcinoma Initially Diagnosed by Inguinal Lymph Node Biopsy

Metastasis from breast cancer to the inguinal lymph nodes or the vulva is a rare event with very few cases reported in literature. A 38-year-old lady presented complaining of progressive abdominal enlargement. Physical examination was unremarkable apart from abdominal enlargement and enlarged right inguinal lymph node. Radiology showed marked ascites, omental thickening, bilateral small ovarian masses, suspicious right inguinal lymph node (LN) as well as breast fibrocystic disease. Cytological examination of the ascitic fluid revealed malignant cells. Core needle biopsy from the inguinal LN suggested metastatic breast carcinoma. Breast MRI showed heterogeneous fibro-glandular tissue and moderate parenchymal enhancement. Random core needle biopsies from the breast led to the diagnosis of invasive lobular carcinoma. The patient received 8 cycles of docetaxel/carboplatin protocol, with partial response. Next, she was maintained on hormonal treatment (Anastrazole) till the ascites re- accumulated. Vulvar nodules appeared whose biopsy revealed infiltration by the same tumoral tissue. She received 6 cycles of Endoxan and Pharmarubicin till regression was achieved, and then she was subjected to Exemestane. Thorough clinical assessment including of all lymph node basins and gynecological assessment are crucial in all breast cancer patients, particularly those with invasive lobular subtypes. Keywords: Breast neoplasms, Lobular, Vulva, Vulvar metastasis, Neoplasm metastasi

Liver morbidity among haemodialysis patients negative for manifest HBV and HCV : A hospital-based study in an endemic area

Background: End-stage renal disease (ESRD) is an increasing health problem worldwide.  Older age, diabetes mellitus and hypertension, acute kidney damage are among some of the factors that play a role in ESRD. This study aims at exploring liver morbidity (LM) among Egyptian hemodialysis (HD) patients.  Methods: The study included 142 patients free from overt hepatitis B virus (HBV) or  hepatitis C virus (HCV); their data were retrieved from files and all were clinically assessed and tested for liver functions, serological markers and viremia of HCV and HBV. Results: Of 142 eligible HD patients, two seroconverted to overt HCV, and five showed occult HCV. According to the laboratory and ultrasonography (US) data, three patterns of LM were found in 62 (43.7%), non-alcoholic fatty liver disease (NAFLD) in 30 (48.4%), liver fibrosis in 19 (30.6%), and hepatitis in 13 (21%). The mean durations of renal impairment (7.6 ± 5.91), hemodialysis (9 ± 6.1269), and total dialysis sessions (976.26 ± 683.69) were significantly higher in patients with LM compared to others (6.78 ± 4.82, 5.38 ± 3.69, and 699.4 ± 467.1). Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) levels were elevated in 14 (22.6%) and 13 (21%) of patients with LM. However, the ROC curve revealed ALT and AST cut-off points of 16.5 and 25.5 IU/L to discriminate LM. Conclusion: LM is common among EDRD patients undergoing hemodialysis despite the low levels of ALT and AST. The use of the US and the new lower levels of ALT and AST could improve the screening approach of LM

Models for COVID-19 Daily Confirmed Cases in Different Countries

In this paper, daily confirmed cases of COVID-19 in different countries are modelled using different mathematical regression models. The curve fitting is used as a prediction tool for modeling both past and upcoming coronavirus waves. According to virus spreading and average annual temperatures, countries under study are classified into three main categories. First category, the first wave of the coronavirus takes about two-year seasons (about 180 days) to complete a viral cycle. Second category, the first wave of the coronavirus takes about one-year season (about 90 days) to complete the first viral cycle with higher virus spreading rate. These countries take stopping periods with low virus spreading rate. Third category, countries that take the highest virus spreading rate and the viral cycle complete without stopping periods. Finally, predictions of different upcoming scenarios are made and compared with actual current smoothed daily confirmed cases in these countries

A Machine Learning Model to Predict Urban Sprawl Using Official Land-use Data

The rate of global urbanization is constantly increasing. As a result of the massive population growth, there is an increasing demand for further urban development, especially in developing regions such as Aswan city. This paper aims to examine the usage official land-use data in predicting future urban growth until 2046, moreover, to define urban driving forces in case study area. This was done using Similarity weighted model, a machine learning based model to simulate future urban growth. The results show that official land-use data produce a slightly better results’ accuracy than remote sensing sources within small to medium scales. The results although reveal that for study region, urban area is expected to expand to cover an area of almost 4460 Feddan by year 2046. The outcome of this research assesses decision makers to accurately predict future urban sprawl areas using available official land-use data