30 research outputs found
Assessing the prospects and challenges of Bangladesh Shipping Corporation in the context of the China\u27s 21st Century Maritime Silk Road initiative
Revisiting sparse dynamic programming for the 0/1 Knapsack Problem
2019 Spring.Includes bibliographical references.The 0/1-Knapsack Problem is a classic NP-hard problem. There are two common approaches to obtain the exact solution: branch-and-bound (BB) and dynamic programming (DP). A socalled, "sparse" DP algorithm (SKPDP) that performs fewer operations than the standard algorithm (KPDP) is well known. To the best of our knowledge, there has been no quantitative analysis of the benefits of sparsity. We provide a careful empirical evaluation of SKPDP and observe that for a "large enough" capacity, C, the number of operations performed by SKPDP is invariant with respect to C for many problem instances. This leads to the possibility of an exponential improvement over the conventional KPDP. We experimentally explore SKPDP over a large range of knapsack problem instances and provide a detailed study of the attributes that impact the performance. DP algorithms have a nice regular structure and are amenable to highly parallel implementations. However, due to the dependence structure, parallelizing SKPDP is challenging. We propose two parallelization strategies (fine-grain and coarse-grain) for SKPDP on modern multi-core processors and demonstrate a scalable improvement in the performance
Emergence, ecology and dispersal of the pandemic generating Vibrio cholerae lineage
Although cholera is an ancient disease that first arose at least half a millennium ago, it remains a major health threatglobally. Its pandemic form is caused by strains from a single lineage of the bacterium Vibrio cholerae. The ancestor of this lineageharbored several distinctive characteristics, the most notable being the O1 antigen polysaccharide. This lineage generatedtwo biotypes, first Classical, responsible for six pandemics, and later El Tor, responsible for the seventh and ongoing pandemic.Just as El Tor replaced Classical as the main cause of outbreaks in the last fifty years, several variants of El Tor have evolved anddisplaced their predecessors worldwide. Understanding the ecology, evolution and dispersal of pandemic V. cholerae is centralto studying this complex disease with environmental reservoirs. Here, we present recent advancements of our knowledge on theemergence and spread of the pandemic generating lineage of V. cholerae in the light of established eco-evolutionary observations.Specific ecological interactions shape seasonal cholera, playing a role in the abundance and distribution of its causative agent.Both species-specific and lineage-specific genetic determinants play a role in the ability of V. cholerae strains to cause pandemicswith seasonal outbreaks, having evolved gradually over centuries. On the basis of the current understanding, we outline futurethreats and changes in biogeographical and genomic-based investigation strategies to combat this global problem
Structure, Morphology and Color Tone Properties of theNeodymium Substituted Hematite
Co-precipitation method has been employed to fabricate neodymium substituted hematite with different compositions from the aqueous solution of their corresponding
metal salts. Thermal analysis and X-ray diffraction studies revealed the coexistence of Fe(2)O(3) and Nd(2)O(3) phases up to 1050℃ and formation of solid solution phase among
them at 1100℃ and above temperatures, which was evidenced by shifting of the XRD peaks. Unit cell parameters and the cell volumes of the samples were found to increase by adding Nd(3+) ions in the reaction process. FESEM studies showed the suppression of particle growth due to the presence of Nd(3+) ions. Spectroscopic measurement evidenced that neodymium substituted hematite exhibited brighter yellowish red color tone than that of pure α-Fe(2)O(3)
Improved Solar Photovoltaic Array Model with FLC Based Maximum Power Point Tracking
This paper presents an improved model of solar photovoltaic (PV) array along with the implementation of fuzzy logic as maximum power point tracking (MPPT). The proposed PV array behavioral model is more accurate and with reduced complexity though considered discrete components. The PV array model was well verified by considering the effect of change of environmental conditions, mainly intensity of solar irradiation (insolation) and temperature. The model was tested by feed a single phase inverter. MPPT control the operating voltage of PV arrays in order to maximize their power output as a result maximize the array efficiency and minimize the overall system cost. Using a Fuzzy logic based algorithm, the duty cycle of the converter inserted between source and load is adjusted continuously to track the MPP and compared with the conventional perturb and observed (P&O) method for changing environmental conditions. It was found that the Fuzzy logic based method can track the MPP more precisely and rapidly than the conventional one. In P&O method, if step size of input variable is very small, the accuracy in tracking MPP is sufficient but tracking speed becomes too slow. On the other hand if the step size is increased to imitate the rapidly changing weather conditions, accuracy deteriorates and unexpected results occur due to oscillation around a mean point although tracking speed increased. But in the case of proposed FLC whatever the step size of input variable it best suited to track MPP continuously and accurately. The obtained simulation results validate the competent of the solar PV array model as well as the fuzzy controller.DOI:http://dx.doi.org/10.11591/ijece.v2i6.132
Manifesting a mobile application on safety which ascertains women salus in Bangladesh
This paper reflects on the indemnity of women in our society. The proposed model ensures the embodiment of a mobile application. The algorithm, we developed for this model focuses the safety issues which is applicable to both inside as well as outside of the house for the women in Bangladesh. The solution of this problems can be done through some interrelated features such as i) SOS button pressing which ensures automatic calling, instant location tracking system through GPS of the phone and sending tracked location to all trusted numbers, automatically secrete video recording system ii) voice command detection which assures exact same features as SOS button pressing iii) phone shaking features serve user instant immunity by calling a trusted number. This research also assures experimented data analysis at Dhaka city based on respond time, the time it takes to arrive the SMS and Phone call and current location of the victim. Also do a short comparison among the most popular safety related mobile applications
Feature selection and prediction of heart disease using machine learning approaches
Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%
Automatic brain tumor detection using feature selection and machine learning from MRI Images
A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis
Comparison Between Reduced Susceptibility to Disinfectants and Multidrug Resistance Among Hospital Isolates of Pseudomonas aeruginosa and Staphylococcus aureus in Bangladesh
Disinfectants have been used largely in hospitals, health care centers and different pharmaceuticals for the removal of microorganisms. It is evident that microorganisms are showing reduced sensitivity against many disinfectants or their minimum inhibitory concentration (MIC) is increasing day by day due to improper use. The aim of this study was to compare the reduced susceptibility to disinfectants and antibiotics of 25 hospital isolates of Pseudomonas aeruginosa and 40 hospital isolates of Staphylococcus aureus isolated from 5 different hospitals at Noakhali region of Bangladesh. Susceptibility of the selected isolates to two disinfectants (savlon and herpic) and ten separate antimicrobial agents for both P. aeruginosa and S. aureus were investigated and compared. Multidrug resistant pattern of all the hospital isolates were determined by agar diffusion method and MIC of the disinfectants were determined by the serial dilution method. All the hospital isolates of P. aeruginosa and S. aureus were multidrug resistant. No severe evident resistance to disinfectants was seen among the 25 isolates of P. aeruginosa and 40 isolates of S. aureus. Interestingly, satisfactory MIC of savlon for 25 isolates of P. aeruginosa and 40 isolates of S. aureus reached at 0.5% to 0.7% (v/v) solution whereas satisfactory MIC of herpic reached at 2% to 2.5% (v/v) solution for all hospital isolates but four isolates of S. aureus showed MIC against herpic at 1.75% (v/v) solution. No sign of co-resistant of disinfectant and antibiotics were found. So, it can be concluded that disinfectants (savlon and herpic) can’t be responsible for P. aeruginosa and S. aureus to become multidrug resistant, when the semi inhibitory dilution of these disinfectants are used