Browsing by Author "Faruque, Md. Omar"

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Genetic characterization of tropical calcific pancretitis in patients with and without diabetes mellitus
(© University of Dhaka, 2025-04-23) Faruque, Md. Omar
Instrumentation and Control Division of Ashuganj Power Station Company Limited (APSCL)
(East West University, 1/1/2013) Rahman, Md. Atikur; Aktaruzzaman; Faruque, Md. Omar
A shortage of electric energy is the biggest problem to the economical growthof any country. The power sector of Bangladesh faced various problems, such as, lack of supply capacity, frequent power cuts, unacceptable generation of power, and poor financial and operational performance etc. In Bangladesh, the present maximum demand of electricity varies from 4,500 MW to MW and it is expected torises up to 7,000 MW within the next two years. But maximumgeneration available is between 3,800 MW and 4,600 MW. The difference between maximum demand and maximum generation of power is approximately 2,000 MW, due to old set-up and de-rated efficiency of the maximum power plants. APSCL where we have done our internship, has 9 units with installed capacity of 777 MW. But its present de-rated capacity is 731 MW and dependable capacity at a delivery point 573 MW. APSCL fulfills about 15% of power requirements of the total country. Manpower at APSCL is almost 517 on regular basis, which plays a vital role in the job market of developing country. a power station can generate and transmit power We work edges turbine gas turbine steam turbine and generation system.
Performance Analysis of Hybrid Plasmonic Waveguide Devices using Highly Doped Semiconductors
(2019-11-15) Faruque, Md. Omar
In this work, an investigation is made to find an alternative material for metals in plasmonic devices in order to overcome the drawbacks of plasmonic devices formed using conventional plasmonic metals like gold and silver. The plasmonic devices formed with gold and silver have a number of limitations in dealing with optical losses, nanofabrication, tunability, chemical stability, and compatibility with conventional manufacturing processes. Thus, to find a suitable replacement, plasmonic properties of gold and silver are studied and materials with similar properties are searched. As a potential alternative material to gold and silver, heavily doped silicon is mathematically modeled and the theoretical model is compared with experimentally obtained data in order to verify the theoretical modeling. The verified model is compared with metals and the plasmonic properties of heavily doped silicon are found similar to those of metals. The material is then defined with the verified model and waveguide is formed using heavily doped silicon instead of metal. The transmission characteristics of the newly formed waveguide are compared with conventional gold and silver waveguide and the suitable material is selected for designing plasmonic devices. Two plasmonic refractive index sensors are investigated numerically and optimized for obtaining a better result. The first refractive index sensor has a highest sensitivity of 1208.9 nm/RIU and the other one has a highest sensitivity of 4900 nm/RIU which is the highest sensitivity reported for plasmonic refractive index sensor to the best of my knowledge. Both the sensors have resolution as small as 0.005. Moreover, the sensors have very simple structures, one with gratings in a straight waveguide and the other with a ring resonator and thus are suitable for being integrated in optical integrated circuits. However, the main advantage of these devices is that, they use only silicon instead of metals like gold or silver which makes them similar to SOI devices and thus, they become compatible with CMOS technology.
Predicting Chronic Kidney Disease of Diabetes Patients Using Ensemble Learning
(6th International Conference on Communication and Electronics Systems (ICCES), IEEE, 2021-08-02) Faruque, Md. Omar; Hossain, Sabbir; Al Marouf, Ahmed
Chronic kidney disease is the reason for many deaths all over the world every year. Chronic kidney disease has troubled almost 753 million people all over the world in 2016, wherein 417 million are females and 336 million are males. In the year 2015, it was the reason for 1.2 million deaths all over the world. When CKD is detected in the later stage of a diabetes patient, it is very harmful to them. Sometimes it leads them to death. But if it is possible to detect chronic kidney disease at an early stage of diabetes patients, the damage can be minimized. This research paper has shown a comparative analysis on the performance of some algorithms - Multilayer Perceptron, Bagging, and Adaboost. And this research work has also used some algorithms such as Bagging (J48), Bagging (Random Tree), Bagging (Decision Stump), Bagging (LMT), Adaboost (Random Tree), Adaboost (Decision Stump), Adaboost (J48), Adaboost (Random Forest). Our comparison of different algorithms will help people having diabetes to figure out whether they will have CKD or not in the future. From all these algorithms Bagging (Random Tree) and AdaBoost (Random Forest) have the best result. By comparing the results of all algorithms, the best algorithm can be detected for predicting the chronic kidney disease. This study can save many people's lives and money. Doctors can also be benefitted from this research.
Prevalence of multidrug-resistant Acinetobacter baumannii in intensive care unit admitted patients from a hospital in Dhaka City, Bangladesh
(BRAC University, 2022-12) Mannan Sharif, Maliha Abdul; Tabassum, Nabila; Faruque, Md. Omar; Haque, Dr. Fahim Kabir Monjurul
Background: Multidrug-resistant Acinetobacter baumannii has become a concern in the world of healthcare. The most common source of infections acquired in hospitals is an opportunistic bacterial pathogen called A. baumannii. It has become a significant nosocomial pathogen that has claimed many lives throughout the world, including in Bangladesh. This study's goals were to determine the pathogen's prevalence and contribute to the development of a local antibiogram database so that future treatment approaches can be improved. Materials and Method: From August 2022 to December 2022, a total of 72 pathogenic Gram negative clinical isolates of Acinetobacter spp. were collected from ICU- admitted patients, and tested at the clinical microbiology laboratory of a private hospital in Dhaka, Bangladesh. Most of the isolates were recovered from tracheal aspirates, sputum, pus, and wounds. The collected isolates were further analyzed at BRACU MNS research laboratory presumptively by cultural methods for the presence of A. baumannii using highly selective Leeds Acinetobacter Medium (LAM) and A. baumannii was confirmed by conventional polymerase chain reaction (PCR) using the primers of blaOXA-51. Findings: From all the 72 clinical isolates, a total of 24 A. baumannii isolates tested positive using conventional Polymerase Chain Reaction (PCR) and Agarose Gel Electrophoresis, determining the prevalence as 33%. All confirmed isolates were characterized by Antimicrobial Susceptibility Testing (AST), performed following the disk diffusion method as recommended by Clinical Laboratory and Standards Institute (CLSI). The A. baumannii isolates confirmed Multidrug resistance (MDR) by showing antimicrobial resistance to more than three antimicrobial categories, such as 100% resistance to Ampicillin (AMP), 70.8% resistance to Cefepime (CPM), Ceftazidime (CAZ), and Levofloxacin (LE), 66.7% resistance to Imipenem (IMP), 62.5% to Gentamicin (GEN), Amikacin (AK) and Tetracycline (TE), and 58.3% resistance Piperacillin-tazobactam (PIT). However, the highest sensitivity result showed 62.5% for Doxycycline (DO) and 58.3% for Trimethoprim-sulfamethoxazole (COT). Conclusion: This research suggests that the supervision of antimicrobial resistance of A. baumannii is essential. The prevalence will help in the implementation of better infection control measures, and a local antibiogram update will increase our awareness of the patterns of antimicrobial resistance in healthcare facilities.