The effect of cyclone on the ocean primary productivity in Bay of Bengal

The understanding about ocean primary productivity is very important as it is a key component of the Earth’s biogeochemical carbon cycles, as well as in predicting the response of marine biota to possible changes in radiative or other physical forcing because of global warming (Wang et al., 2009). Chlorophyll-a has been known as the most important indicator for ocean productivity estimation (Nagamani et al., 2011). Many studies have been conducted to determine the variation of chlorophyll-a concentration due to several factors such as cyclone (Tripathy et al., 2012). This study focuses on examining the effect of various cyclone events on the ocean primary productivity in Bay of Bengal (BOB). BOB is the largest bay in the world with unique semi-enclosed tropical basin, monsoon variation, and experienced high rainfall and frequent cyclone (Reddy et al., 2008). Tripathy et al., 2012 had found the frequent occurrence of cyclonic events causes shortterm nutrient enrichment of upper-stratified ocean resulting in enhanced biological productivity. Upwelling and entrainment processes due to strong winds associated with cyclone will bring up nutrients and causes phytoplankton bloom (Reddy et al., 2008). Chlorophyll concentration is increased under the cyclone track and the blooms of phytoplankton were covered wide area (Smitha et al., 2006)

Monitoring long-term ocean health using remote sensing: A case study of the Bay of Bengal

Oceans play a significant role in the global carbon cycle and climate change, and the most importantly it is a reservoir for plenty of protein supply, and at the center of many economic activities. Ocean health is important and can be monitored by observing different parameters, but the main element is the phytoplankton concentration (chlorophyll-a concentration) because it is the indicator of ocean productivity. Many methods can be used to estimate chlorophyll-a (Chl-a) concentration, among them, remote sensing technique is one of the most suitable methods for monitoring the ocean health locally, regionally and globally with very high temporal resolution. In this research, long term ocean health monitoring was carried out at the Bay of Bengal considering three facts i.e. i) very dynamic local weather (monsoon), ii) large number of population in the vicinity of the Bay of Bengal, and iii) the frequent natural calamities (cyclone and flooding) in and around the Bay of Bengal. Data (ten years: from 2001 to 2010) from SeaWiFS and MODIS were used. Monthly Chl-a concentration was estimated from the SeaWiFS data using OC4 algorithm, and the monthly sea surface temperature was obtained from the MODIS sea surface temperature (SST) data. Information about cyclones and floods were obtained from the necessary sources and in-situ Chl-a data was collected from the published research papers for the validation of Chl-a from the OC4 algorithm. Systematic random sampling was used to select 70 locations all over the Bay of Bengal for extracting data from the monthly Chl-a and SST maps. Finally the relationships between different aspects i.e. i) Chl-a and SST, ii) Chl-a and monsoon, iii) Chl-a and cyclones, and iv) Chl-a and floods were investigated monthly, yearly and for long term (i.e 10 years). Results indicate that SST, monsoon, cyclone, and flooding can affect Chl-a concentration but the effect of monsoon, cyclone, and flooding is temporal, and normally reduces over time. However, the effect of SST on Chl-a concentration can't be minimized very quickly although the change of temperature over this period is not very large.Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2013 (24 September 2013, Dresden, Germany

COVID-19 Pandemic: A Comparative Prediction Using Machine Learning

Coronavirus Disease 2019 or COVID-19 is an infectious disease which is declared as a pandemic by the World Health Organization (WHO) have a noxious effect on the entire human civilization. Each and every day the number of infected people is going higher and higher and so the death toll. Many of country Italy, UK, USA was affected badly, yet since the identification of the first case, after a certain number of days, the scenario of infection rate has been reduced significantly. However, a country like Bangladesh couldn't keep the infection rate down. A number of algorithms have been proposed to forecast the scenario in terms of the number of infection, recovery and death toll. Here, in this work, we present a comprehensive comparison based on Machine Learning to predict the outbreak of COVID-19 in Bangladesh. Among Several Machine Learning algorithms, here we used Polynomial Regression (PR) and Multilayer Perception (MLP) and Long Short Term Memory (LSTM) algorithm and epidemiological model Susceptible, Infected and Recovered (SIR), projected comparative outcomes

Evaluation of HPV-DNA Test in Detection of Precancerous and Cancerous Lesions of Cervix

ABSTRACT The knowledge that cervical neoplasia are caused by Human Papillomavirus (HPV) infection has led to the evaluation of its role in screening of cervical neoplasia. This study was carried out to evaluate the accuracy of HPV-DNA test in diagnosis of precancerous and cancerous lesions of cervix in relation to histopathology. Total no of 115 eligible women were included in this study. After recording relevant data cervix was examined on naked eye by cuscos speculum. Paps smear collection and VIA tests were done concurrently. Colposcopic examination was done who were positive in screening tests. In addition, subjects with grossly abnormal cervix even with negative in screening tests were also referred for colposcopy. Samples for HPV DNA were taken from the patients referred for colposcopy and biopsies were done in the same patients. Those with CIN I or worse lesions diagnosed by histology were considered as true positive. The study results showed the test parameters for VIA were sensitivity of 94.11%, specificity of 57.57%, positive predictive value of 12.20%, and negative predictive value of 99.70%. The test parameters for Pap smear were sensitivity of 64.71%, specificity of 94.29%, positive predictive value of 51.70% and negative predictive value of 99.80%. The test parameters for HPV DNA test were sensitivity of 82.35% and specificity of 84.85%, positive predictive value of 73.68% and negative predictive value of 90.32%. VIA and HPV-DNA tests detected all cases of high grade lesions (CIN II & III) and carcinoma. This study was that VIA is superior to Pap smear cytology and HPV-DNA test in sensitivity, that is VIA can more accurately identify the CIN/ cancer patients, On the other and Pap smear is superior to VIA and HPV-DNA test in specificity that it can more accurately identify the truly well people and HPV-DNA has strong association in high grade lesions of the cervix

Impact of Internal Factors on the Profitability of Banks: A Case of Commercial Banks in Bangladesh

The internal factors of the bank have a great influence on the profitability of the banks. This study is an effort to disclose the effect of bank’s internal factors on return on equity (ROE), return on asset (ROA), and net interest margin (NIM) of ten selected commercial banks in Bangladesh for the period of 2011-2015. Researchers used descriptive statistics, correlation and regression analysis as statistics tools to find out the results. The findings from descriptive statistics indicate that Eastern Bank Limited was ranked first regarding profitability. The correlation test found that total equity to total asset ratio (TETA) and cost to income ratio (CIR) significantly affects the ROA whereas loan to deposit ratio had significant positive effect on the NIM of the banks. The regression analysis revealed that the independent variables of the banks were significant enough to explain the variation of the dependent variables (ROA, ROE, and NIM) of the study

ToSHI - Towards Secure Heterogeneous Integration: Security Risks, Threat Assessment, and Assurance

The semiconductor industry is entering a new age in which device scaling and cost reduction will no longer follow the decades-long pattern. Packing more transistors on a monolithic IC at each node becomes more difficult and expensive. Companies in the semiconductor industry are increasingly seeking technological solutions to close the gap and enhance cost-performance while providing more functionality through integration. Putting all of the operations on a single chip (known as a system on a chip, or SoC) presents several issues, including increased prices and greater design complexity. Heterogeneous integration (HI), which uses advanced packaging technology to merge components that might be designed and manufactured independently using the best process technology, is an attractive alternative. However, although the industry is motivated to move towards HI, many design and security challenges must be addressed. This paper presents a three-tier security approach for secure heterogeneous integration by investigating supply chain security risks, threats, and vulnerabilities at the chiplet, interposer, and system-in-package levels. Furthermore, various possible trust validation methods and attack mitigation were proposed for every level of heterogeneous integration. Finally, we shared our vision as a roadmap toward developing security solutions for a secure heterogeneous integration

Remote sensing technique for estimating the age of oil palm using high resolution image

Oil palm is a tropical crop that mainly grows for its industrial production and has become one of the most economic crops in some countries especially in Malaysia and Indonesia. Although oil palm is one of Malaysia's major sources of revenue, there are lots of controversy about the impact of this massive cultivation on the environment. Age of the Oil Palm is one of the most important factors that plays a significant role in the productivity of the oil palm as well as for the impact assessment of oil palm plantation on carbon sequestration process. Although the age of the oil palm is available to the respective oil palm companies, this information is very hard to obtain collectively from the different oil palm companies for research purposes. Therefore, this study is going to use remote sensing technique for the estimation of the age of the oil palm using a high-resolution satellite data (Worldview-2). The complexity of the determination of oil palm age from the satellite data is that age information cannot be extracted easily due to the similar spectral signature among the different oil palm age groups as well as confusion with the spectral signature of forest and other features. Various image processing techniques such as band rationing, vegetation indices and texture measurement were carried out for generating variable image parameters from the original data in order to overcome the spectral confusion among the different age groups as well as with other features. All the image parameters were classified using Maximum Likelihood Classifier (MLC) and Artificial Neural Network (ANN). The results of the classification were validated using the ground truth data, and comparison of different results was made to find the best technique that can be used for the determination of the oil palm age effectively. The result indicates that multispectral band is helpful to determine the age of the oil palm, but obtained accuracy was not promising because of the similar spectral signature among the different age classes, however, the classification accuracy was improved when the textural information was used in the classification algorithm. This research found that texture measurement is very promising, and it has the potential to differentiate different features as well as the age group of oil palm plantation, and an accuracy of 60% was obtained using multi-scale and multi-texture approach

Exploring the potential of high resolution remote sensing data for mapping vegetation and the age groups of oil palm plantation

The land use/land cover transformation in Malaysia is enormous due to palm oil plantation which has provided huge economical benefits but also created a huge concern for carbon emission and biodiversity. Accurate information about oil palm plantation and the age of plantation is important for a sustainable production, estimation of carbon storage capacity, biodiversity and the climate model. However, the problem is that this information cannot be extracted easily due to the spectral signature for forest and age group of palm oil plantations is similar. Therefore, a noble approach "multi-scale and multi-texture algorithms" was used for mapping vegetation and different age groups of palm oil plantation using a high resolution panchromatic image (WorldView-1) considering the fact that pan imagery has a potential for more detailed and accurate mapping with an effective image processing technique. Seven texture algorithms of second-order Grey Level Co-occurrence Matrix (GLCM) with different scales (from 3×3 to 39×39) were used for texture generation. All texture parameters were classified step by step using a robust classifier "Artificial Neural Network (ANN)". Results indicate that single spectral band was unable to provide good result (overall accuracy = 34.92%), while higher overall classification accuracies (73.48%, 84.76% and 93.18%) were obtained when textural information from multi-scale and multi-texture approach were used in the classification algorithm

Ocean primary productivity variation due to the cyclone: a case study at Bay of Bengal

Monitoring ocean primary productivity especially Chlorophyll-a (Chl-a) concentration is important as it contributes to the carbon cycle, global climate change and ocean health study. This study aims to examine the effects of cyclone events on the ocean productivity in the Bay of Bengal (BOB) considering its importance on global climate change. Level 2 SeaWiFS daily data from 2001 to 2010 were used to determine Chl-a concentration and data from the Indian Meteorological Department (IMD) were used to get information and locations of the cyclone events. Variation of Chl-a concentration was determined from the Chl-a concentration maps (pre-, during, and post-cyclone) using several transect lines parallel to the cyclone passages. Results indicated that there is a relationship between the variation of Chl-a concentration and the cyclone events at the BOB but the effect is varied according to the type of cyclone where very severe cyclonic storm (VSCS) has higher impact on Chl-a concentration compared to cyclonic storm (CS) and severe cyclonic storm (SCS). In most cases, Chl-a concentration was increased right after the cyclone event and the influence was observed over a wide area surrounding the cyclone passage.Monitoring ocean primary productivity especially Chlorophyll-a (Chl-a) concentration is important as it contributes to the carbon cycle, global climate change and ocean health study. This study aims to examine the effects of cyclone events on the ocean productivity in the Bay of Bengal (BOB) considering its importance on global climate change. Level 2 SeaWiFS daily data from 2001 to 2010 were used to determine Chl-a concentration and data from the Indian Meteorological Department (IMD) were used to get information and locations of the cyclone events. Variation of Chl-a concentration was determined from the Chl-a concentration maps (pre-, during, and post-cyclone) using several transect lines parallel to the cyclone passages. Results indicated that there is a relationship between the variation of Chl-a concentration and the cyclone events at the BOB but the effect is varied according to the type of cyclone where very severe cyclonic storm (VSCS) has higher impact on Chl-a concentration compared to cyclonic storm (CS) and severe cyclonic storm (SCS). In most cases, Chl-a concentration was increased right after the cyclone event and the influence was observed over a wide area surrounding the cyclone passage.Monitoring ocean primary productivity especially Chlorophyll-a (Chl-a) concentration is important as it contributes to the carbon cycle, global climate change and ocean health study. This study aims to examine the effects of cyclone events on the ocean productivity in the Bay of Bengal (BOB) considering its importance on global climate change. Level 2 SeaWiFS daily data from 2001 to 2010 were used to determine Chl-a concentration and data from the Indian Meteorological Department (IMD) were used to get information and locations of the cyclone events. Variation of Chl-a concentration was determined from the Chl-a concentration maps (pre-, during, and post-cyclone) using several transect lines parallel to the cyclone passages. Results indicated that there is a relationship between the variation of Chl-a concentration and the cyclone events at the BOB but the effect is varied according to the type of cyclone where very severe cyclonic storm (VSCS) has higher impact on Chl-a concentration compared to cyclonic storm (CS) and severe cyclonic storm (SCS). In most cases, Chl-a concentration was increased right after the cyclone event and the influence was observed over a wide area surrounding the cyclone passag